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The 10 Attributes that Drive Adoption and Diffusion of Computational Tools in E-Science

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Published:17 July 2016Publication History

ABSTRACT

As the computational movement gains more traction in the scientific community, there is an increasing need to understand what drives adoption and diffusion of tools. This investigation reveals what makes a computational tool more easily adopted by users within the e-science community. Guided by Rogers's [1] Diffusion of Innovations theory, we set out to identify the innovation attributes of a range of computational tools across domains. Based on 135 interviews with domain scientists, computational technologists, and supercomputer center administrators across the U.S. and a small portion from Europe, systematic analysis revealed 10 key attributes of tools. They are: driven by needs, organized access, trialability, observability, relative advantage, simplicity, compatibility, community-driven, well-documented, and adaptability. We discuss the attributes in the form of questions stakeholders should keep in mind while designing and promoting the tools. We also present diffusion strategies associated with each attribute. The 10 attributes and associated questions can serve as a checklist for e-science projects that aim to promote their computation tools beyond the incubators. This paper is submitted to the "Software and Software Environments" track because it has implications for engagement of user communities.

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  • Published in

    cover image ACM Other conferences
    XSEDE16: Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale
    July 2016
    405 pages
    ISBN:9781450347556
    DOI:10.1145/2949550

    Copyright © 2016 ACM

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    New York, NY, United States

    Publication History

    • Published: 17 July 2016

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