Prediction of design team performance through analysis of language use in meetings

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In the research to be reported here, there has been developed a new, performance-predictive method of examining how team members interact in design meetings. It is based on an already--developed measure of how we adjust and coordinate our use of language when interacting, called the Language Style Matching (LSM) measure (Gonzales et al., 2010). The LSM measure has already been shown to give insight into peoples' psychology and ways of interacting. The insight available from the LSM measure is due to a well-established aspect of human behavior: when we interact, we adjust our behavior in response to each other in myriad ways. This is fundamental to who we are as social beings. We use these ways, both consciously and unconsciously, to develop ideas together, to plan together, to play and work together — for all our coordination in groups. We also adjust our language use toward one another. This adjustment — described with such terms as "language matching" and "entrainment" — has been shown in a number of studies to be predictive of the outcome of task-oriented conversations. The LSM measure is one proven way to measure this adjustment. Design-oriented meetings and conversations, though, have not been analyzed in these terms. For this research, two corpora of face-to-face design meeting transcripts are analyzed: one set is of a group of researchers meeting over time to develop computer systems to analyze language; the other set is of 13 different teams working on the conceptual re-design of a handheld sensor device. The results of the latter teams' work were rated by a group of expert designers, as a way of assessing the relative performance of the teams. Through the application of some simple statistical methods, this research has uncovered some new patterns in the language used in the design meeting corpora. They are based on measures of language use derived from and stronger than than those from the original LSM measure. Several of the new patterns are shown to be predictive of the quality of the design teams' work in the performance-rated second corpus. Evidence is also given that the new measures correlate with several aspects of the meetings that differ between the two corpora, such as pace of the meetings and level of acquaintance of the participants. Many of the ways that design teams have been studied in the past require a great deal of intensive effort by well-trained researchers. Their high cost limits the quantity and variety of teams, settings, projects, etc. that can be studied and compared. In turn, this limits our efforts to understand and improve teams' dynamics. Like the LSM measure it is based on, the new method of analysis needs only transcripts of conversations for analysis, is topic-independent, and is comparatively inexpensive to use. As a result, it can be the basis for examining and comparing interpersonal dynamics and performance in design teams as they work in a large variety of situations, towards different goals. In this dissertation strengths and limitations of this new method are also discussed. Used as a complement to more detail-oriented analyses, the method promises a breadth of view on design work that hasn't been available before.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2018; ©2018
Publication date 2018; 2018
Issuance monographic
Language English


Author Cannon, David Maeser
Degree supervisor Leifer, Larry J
Thesis advisor Leifer, Larry J
Thesis advisor Cutkosky, Mark R
Thesis advisor Ju, Wendy, 1975-
Degree committee member Cutkosky, Mark R
Degree committee member Ju, Wendy, 1975-
Associated with Stanford University, Department of Mechanical Engineering.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility David Maeser Cannon.
Note Submitted to the Department of Mechanical Engineering.
Thesis Thesis Ph.D. Stanford University 2018.
Location electronic resource

Access conditions

© 2018 by David Maeser Cannon
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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