Design Team Performance: Context, Measurement, and the Prospective Impact of Social Virtual Reality

Ade Mabogunje (), Neeraj Sonalkar (), Mark Miller () and Jeremy Bailenson ()
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Ade Mabogunje: Stanford University
Neeraj Sonalkar: Stanford University
Mark Miller: Stanford University
Jeremy Bailenson: Stanford University

A chapter in Design Thinking Research, 2021, pp 177-201 from Springer

Abstract: Abstract Measuring design performance for effective support of team coaching and redesign efforts has been difficult to near impossible. This is because, design is context dependent and takes place in different environments. Virtual reality gives us as designers, the opportunity to construct and simulate different environments, as coaches, the opportunity to improve our effectiveness in different design scenarios, and as researchers, the possibility to measure design performance and factors that affect it. This possibility was investigated experimentally. Two environments were constructed—one corresponding to a garage in a rural setting, and the other, to a conference room on one of the floors of a skyscraper in the city. Teams of three designers were recruited for the experiment. They worked on two product concept generation tasks and two decision making tasks, while being situated in each of the spaces. Several types of data were collected including video records, screen records, participant questionnaires, and position data of the VR headset and the hand controllers. The position data was used to calculate the level of synchrony between the designers. To investigate the correlation between the synchrony scores and the environment, we used a Kruskal-Wallis ordinal test. The test showed that the teams in the conference room had significantly higher synchrony (H(1) = 7.056, p = 0.0079) than the teams in the garage. This data was surprising, unexpected, and difficult to explain. In the course of searching for an explanation, several earlier models of behavior and context from the literature were reviewed. This led to the development of a comprehensive model of human-environment interaction which we believe will help guide future experiments. Early prototypes of this model are presented and discussed.

Date: 2021
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DOI: 10.1007/978-3-030-62037-0_8

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