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Influencers in design teams: a computational framework to study their impact on idea generation

Published online by Cambridge University Press:  14 October 2021

Harshika Singh Open the ORCID record for Harshika Singh [Opens in a new window] ,
Gaetano Cascini  and
Christopher McComb
Harshika Singh*
Affiliation:
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
Gaetano Cascini
Affiliation:
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
Christopher McComb
Affiliation:
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
*
Author for correspondence: Harshika Singh, E-mail: harshika.singh@polimi.it
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Abstract

It is known that wherever there is human interaction, there is social influence. Here, we refer to more influential individuals as “influencers”, who drive team processes for better or worst. Social influence gives rise to social learning, the propensity of humans to mimic the most influential individuals. As individual learning is affected by the presence of an influencer, so is an individual's idea generation . Examining this phenomenon through a series of human studies would require an enormous amount of time to study both individual and team behaviors that affect design outcomes. Hence, this paper provides an agent-based approach to study the effect of influencers during idea generation. This model is supported by the results of two empirical experiments which validate the assumptions and sustain the logic implemented in the model. The results of the model simulation make it possible to examine the impact of influencers on design outcomes, assessed in the form of exploration of design solution space and quality of the solution. The results show that teams with a few prominent influencers generate solutions with limited diversity. Moreover, during idea generation, the behavior of the teams with uniform distribution of influence is regulated by their team members' self-efficacy.

Keywords

agent-based approachcollaborative designdesign outcomesexplorationidea generationinfluencersquality
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 3 , August 2021 , pp. 332 - 352
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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