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Decision-Making Amplification Under ...

Campbell, Merle Wayne.

Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems.
Author:	Campbell, Merle Wayne.
Description:	113 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Notes:	Adviser: Mark Keil.
Contained By:	Dissertation Abstracts International74-10B(E).
Subject:	Information technology.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3567147
ISBN:	9781303187872

Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems.
Campbell, Merle Wayne.

Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems. - 113 p.

Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.

Thesis (E.D.B.)--Georgia State University, 2013.

This item must not be sold to any third party vendors.

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these "intelligent" systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed.

ISBN: 9781303187872Subjects--Topical Terms:

184390
Information technology.

Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems.
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100 1 $a Campbell, Merle Wayne. $3 708603
245 1 0 $a Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems.
300 $a 113 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Mark Keil.
502 $a Thesis (E.D.B.)--Georgia State University, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these "intelligent" systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed.
520 $a Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency.
520 $a The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context.
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773 0 $t Dissertation Abstracts International $g 74-10B(E).
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