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Computational and empirical explorat...

Ashworth, Michael J.

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Computational and empirical explorations of work group performance.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational and empirical explorations of work group performance./
作者:	Ashworth, Michael J.
面頁冊數:	147 p.
附註:	Adviser: Kathleen M. Carley.
Contained By:	Dissertation Abstracts International68-08B.
標題:	Business Administration, Management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3274195
ISBN:	9780549164814

Computational and empirical explorations of work group performance.
Ashworth, Michael J.

Computational and empirical explorations of work group performance. - 147 p.

Adviser: Kathleen M. Carley.

Thesis (Ph.D.)--Carnegie Mellon University, 2007.

Computational methods combined with traditional empirical techniques offer a powerful new approach to the study of human performance. In this collection of three studies, I first review the state of modern computational modeling and find a steady increase in the incorporation of dynamic, adaptive, and realistic behaviors of agents in social network settings. However, my analysis suggests areas that can be addressed in the next generation of organizational simulation systems. I compare 28 models according to more than 200 evaluation criteria, ranging from simple representations of agent demographic and performance characteristics, to more richly defined instantiations of behavioral attributes, interaction with non-agent entities, model flexibility, communication channels, simulation types, knowledge, transactive memory, task complexity, and resource networks. I assess trends across these criteria, discuss practical applications, and propose an agenda for future research and development.

ISBN: 9780549164814Subjects--Topical Terms:

626628
Business Administration, Management.

Computational and empirical explorations of work group performance.
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245 1 0 $a Computational and empirical explorations of work group performance.
300 $a 147 p.
500 $a Adviser: Kathleen M. Carley.
500 $a Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5539.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2007.
520 $a Computational methods combined with traditional empirical techniques offer a powerful new approach to the study of human performance. In this collection of three studies, I first review the state of modern computational modeling and find a steady increase in the incorporation of dynamic, adaptive, and realistic behaviors of agents in social network settings. However, my analysis suggests areas that can be addressed in the next generation of organizational simulation systems. I compare 28 models according to more than 200 evaluation criteria, ranging from simple representations of agent demographic and performance characteristics, to more richly defined instantiations of behavioral attributes, interaction with non-agent entities, model flexibility, communication channels, simulation types, knowledge, transactive memory, task complexity, and resource networks. I assess trends across these criteria, discuss practical applications, and propose an agenda for future research and development.
520 $a In the second study, using a modified version of one of the models reviewed in the first study, I examine the link between individual performance and group outcomes. Using newly created network-based measures of individual knowledge and task exclusivity along with simulations based on empirical data obtained from a software development firm, I investigate the relative effectiveness of social network theory and resource dependency theory as predictors of individuals' contributions to group performance. Results indicate that individual impacts on group performance are even more closely associated with knowledge and task dimensions than with social network structure. Furthermore, given that knowledge may be assessed a priori, I explore factors that may provide useful guidance for structuring teams and predicting team performance.
520 $a In the third study, I integrate psychological, social network, and organizational learning theories to investigate the extent to which the amount and structure of a work group's transactive memory explains the relationship between the group's collective experience and its performance. I use network surveys and archival data obtained from 1,456 individuals and 87 managers on 118 diverse work teams in four Fortune 500 companies as the basis for analyzing relationships between transactive memory, group experience, and group performance. I find that group transactive memory partially mediates the link between group experience and performance. In addition, the degree of "small-worldness" exhibited in the structure of group transactive memory moderates the memory's mediating effect on group learning. I discuss the study's findings, practical implications, and limitations as well as how the findings may extend to inter-group, organization, interorganizational, and even societal levels.
520 $a Finally, I provide a path for computational extensions of these studies in future research efforts. In particular, I propose modifications of the multi-agent model used in the second study ("Construct") that will enable dynamic simulation of agent-level learning that builds to group and organizational level learning. I also suggest a step-by-step methodology for calibrating, validating, and conducting a multi-agent simulation-based study of organizational learning.
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