國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Decision-Making Amplification Under ...

Campbell, Merle Wayne.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems.
作者:	Campbell, Merle Wayne.
面頁冊數:	113 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
附註:	Adviser: Mark Keil.
Contained By:	Dissertation Abstracts International74-10B(E).
標題:	Information technology.
電子資源:	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.
LDR:03397nmm a2200325 4500 001 457634
005 20150805065211.5
008 150916s2013 ||||||||||||||||| ||eng d
020 $a 9781303187872
035 $a (MiAaPQ)AAI3567147
035 $a AAI3567147
040 $a MiAaPQ $c MiAaPQ
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.
590 $a School code: 0079.
650 4 $a Information technology. $3 184390
650 4 $a Finance. $3 183252
650 4 $a Information science. $3 190425
690 $a 0489
690 $a 0508
690 $a 0723
710 2 $a Georgia State University. $b Business. $3 708604
773 0 $t Dissertation Abstracts International $g 74-10B(E).
790 $a 0079
791 $a E.D.B.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3567147