Human-Centric Decision Support Tools: Insights from Real-World Design and Implementation

TL;DR

This paper emphasizes the importance of stakeholder-centered design in creating effective decision support tools, highlighting how listening to user needs enhances trust, acceptance, and decision-making outcomes across various contexts.

Contribution

It advocates for stakeholder-focused design approaches in decision support tools, emphasizing trust-building and practical lessons from real-world examples.

Findings

Stakeholder engagement improves tool acceptance.

Trust is critical for successful adoption.

Listening to user needs enhances decision outcomes.

Abstract

Decision support tools enable improved decision-making for challenging decision problems by empowering stakeholders to process, analyze, visualize, and otherwise make sense of a variety of key factors. Their intentional design is a critical component of the value they create. All decision-support tools share in common that there is a complex decision problem to be solved for which decision-support is useful, and moreover, that appropriate analytics expertise is available to produce solutions to the problem setting at hand. When well-designed, decision support tools reduce friction and increase efficiency in providing support for the decision-making process, thereby improving the ability of decision-makers to make quality decisions. On the other hand, the presence of overwhelming, superfluous, insufficient, or ill-fitting information and software features can have an adverse effect on the decision-making process and, consequently, outcomes. We advocate for an innovative, and perhaps overlooked, approach to designing effective decision support tools: genuinely listening to the project stakeholders, to ascertain and appreciate their real needs and perspectives. By prioritizing stakeholder needs, a foundation of mutual trust and understanding is established with the design team. We maintain this trust is critical to eventual tool acceptance and adoption, and its absence jeopardizes the future use of the tool, which would leave its analytical insights for naught. We discuss examples across multiple contexts to underscore our collective experience, highlight lessons learned, and present recommended practices to improve the design and eventual adoption of decision dupport tools.