Consumers can choose from many different products and base their decisions on the tens of thousands of online evidence about each of their options. However, to synthesize this information into confident decisions can incur high interaction and cognitive costs. Online information is scattered across different sources, and evidence such as reviews can be subjective and conflicting, requiring users to interpret them under their personal context. We introduce Mesh, which scaffolds users in iteratively building up a better understanding of both their choices by evaluating evidence gathered across sources. Lab and field deployment studies found that Mesh significantly reduces the costs of gathering and evaluating evidence and scaffolds decision-making through personalized criteria enabling users to gain deeper insights from data to make confident purchase decisions.

Abstract

While there is an enormous amount of information online for making decisions such as choosing a product, restaurant, or school, it can be costly for users to synthesize that information into confident decisions. Information for users’ many different criteria needs to be gathered from many different sources into a structure where they can be compared and contrasted. The usefulness of each criterion for differentiating potential options can be opaque to users, and evidence such as reviews may be subjective and conflicting, requiring users to interpret each under their personal context. We introduce Mesh, which scaffolds users in iteratively building up a better understanding of both their criteria and options by evaluating evidence gathered across sources in the context of consumer decision making. Mesh bridges the gap between decision support systems that typically have rigid structures and the fluid and dynamic process of exploratory search, changing the cost structure to provide increasing payoffs with greater user investment. Our lab and field deployment studies found evidence that Mesh significantly reduces the costs of gathering and evaluating evidence and scaffolds decision-making through personalized criteria enabling users to gain deeper insights from data.

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Joseph Chee Chang, Nathan Hahn, Aniket Kittur.
Mesh: Scaffolding Comparison Tables for Online Decision Making.
In Proceedings of the 33rd ACM User Interface Software and Technology Symposium: UIST 2020.

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@article{chang2020mesh,
  title={Mesh: Scaffolding Comparison Tables for Online Decision Making},
  author={Chang, Joseph Chee and Hahn, Nathan, and Kittur, Aniket},
  booktitle = {Proceedings of the 33rd ACM User Interface Software and Technology Symposium},
  series = {UIST'20},
  year={2020}
}