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Publications

  1. Shergadwala, M., Bilionis, I., and Panchal, J. H., 2019. “Quantifying the influence of information sharing about competitor's performance on a participant's sequential design behaviors in design contests.” ASME Journal of Mechanical Design. (under review) [link]

  2. Shergadwala, M., Forbes, H., Schaefer, D., and Panchal, J. H., 2019. “Challenges and Research Directions in Crowdsourcing for Engineering Design: An Interview Study with Industry Professionals.” IEEE Transactions on Engineering Management. (under review) [link]

  3. Shergadwala, M., Bilionis, I., Kannan, K. N., and Panchal, J. H., 2018. “Quantifying the impact of domain knowledge and problem framing on sequential decisions in engineering design.” ASME Journal of Mechanical Design. 2018; 140(10):101402-101402-13. [link]

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For more please refer to my CV

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Bi, Y., Shergadwala, M., Reid, T., and Panchal, J. H. (2015, August). "Understanding the utilization of information stimuli in design decision making using eye gaze data." In Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp.V02AT03A017 - V02AT03A017).

UNDERSTANDING THE UTILIZATION OF INFORMATION STIMULI IN DESIGN

DECISION MAKING USING EYE GAZE DATA

Research on decision making in engineering design has focused primarily on how to make decisions using normative models given certain information. However, there exists a research gap on how diverse information stimuli are combined by designers in decision making. In this paper, we address

the following question: how do designers weigh different information stimuli to make decisions in engineering design contexts? The answer to this question can provide insights on diverse cognitive models for decision making used by different individuals. We investigate the information gathering behavior of individuals using eye gaze data from a simulated engineering design task. The task involves optimizing an unknown function using an interface which provides two types of information stimuli, including a graph and a list area. These correspond to the graphical stimulus and numerical stimulus, respectively. The study was carried out using a set of student subjects. The results suggest that individuals weigh different forms of information stimuli differently. It is observed that graphical information stimulus assists the participants in optimizing the function with a higher accuracy. This study contributes to our understanding of how diverse information stimuli are utilized by design engineers to make decisions. The improved understanding of cognitive decision making models would also aid in improved design of decision support tools. 

 

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