Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    The Effect of Role Specialization And Transactive Memory Systems On Performance in Data Science Teams

    Thumbnail
    View/Open
    Choi_umd_0117E_21273.pdf (681.8Kb)
    (RESTRICTED ACCESS)
    No. of downloads: 0

    Date
    2020
    Author
    Choi, Joohee
    Advisor
    Tausczik, Yla
    DRUM DOI
    https://doi.org/10.13016/hdz3-ab96
    Metadata
    Show full item record
    Abstract
    Teamwork is an integral part of data science work. Data science work requires knowledge from many different disciplines including statistics, information visualization, programming, and subject matter knowledge related to a given set of data sets (e.g., politics, education). Data science teams are often formed by individuals who have different areas of knowledge and expertise and, as a result, may take on different functional roles within a team. Due to their distinctive expertise, members in data science teams may take on specialized task roles matching their expertise, and such division of labor could increase coordination cost among team members. As data science work is often open-ended and dynamic by nature, high coordination costs could deteriorate performance in data science teams. In this research, I argued that developing shared cognition on who-knows-what (i.e., transactive memory system, abbreviated as TMS) in data science teams would be beneficial for team performance, especially when the members have specialized roles. I conducted two studies to understand the effect of role specialization and transactive memory systems on team performance with a goal to identify and test a lever to facilitate transactive memory system in data science teams. I collected data from two consecutive Data Challenge events; Data Challenge is an week-long data science competition hosted annually as a university-wide event. In Study 1, I conducted an observational study by collecting survey data from 74 individuals in 36 teams in Data Challenge 2019. In Study 2, I conducted a field experiment to examine the effectiveness of an experimental intervention designed to facilitate transactive memory system in data science teams by highlighting any inaccuracies in the perceived expertise between members.
    URI
    http://hdl.handle.net/1903/26739
    Collections
    • Information Studies Theses and Dissertations
    • UMD Theses and Dissertations

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility