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    Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams

    Source: Journal of Mechanical Design:;2022:;volume( 144 ):;issue: 010::page 104501-1
    Author:
    Gyory
    ,
    Joshua T.;Kotovsky
    ,
    Kenneth;McComb
    ,
    Christopher;Cagan
    ,
    Jonathan
    DOI: 10.1115/1.4054723
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This brief extends prior research by the authors on studying the impacts of interventions provided by either a human or an artificial intelligence (AI) process manager on team behaviors. Our earlier research found that a created AI process manager matched the capabilities of human process management. Here, these data are studied further to identify the impact of different types of interventions on team behaviors and outcomes. This deeper dive is done via two unique perspectives: comparing teams’ problem-solving processes before and after interventions are provided, and through a regression analysis between intervention counts and performance. Results show overall mixed adherence to the provided interventions, and that this adherence also depends on the intervention type. The most significant impact on the team process arises from the communication frequency interventions. Furthermore, a regression analysis identifies the interventions with the greatest correlation with team performance, indicating a better selection of interventions from the AI process manager. Paired together, the results show the feasibility of automated process management via AI and shed light on the effective implementation of intervention strategies for future development and deployment.
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      Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4287321
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    contributor authorGyory
    contributor authorJoshua T.;Kotovsky
    contributor authorKenneth;McComb
    contributor authorChristopher;Cagan
    contributor authorJonathan
    date accessioned2022-08-18T13:02:32Z
    date available2022-08-18T13:02:32Z
    date copyright6/30/2022 12:00:00 AM
    date issued2022
    identifier issn1050-0472
    identifier othermd_144_10_104501.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4287321
    description abstractThis brief extends prior research by the authors on studying the impacts of interventions provided by either a human or an artificial intelligence (AI) process manager on team behaviors. Our earlier research found that a created AI process manager matched the capabilities of human process management. Here, these data are studied further to identify the impact of different types of interventions on team behaviors and outcomes. This deeper dive is done via two unique perspectives: comparing teams’ problem-solving processes before and after interventions are provided, and through a regression analysis between intervention counts and performance. Results show overall mixed adherence to the provided interventions, and that this adherence also depends on the intervention type. The most significant impact on the team process arises from the communication frequency interventions. Furthermore, a regression analysis identifies the interventions with the greatest correlation with team performance, indicating a better selection of interventions from the AI process manager. Paired together, the results show the feasibility of automated process management via AI and shed light on the effective implementation of intervention strategies for future development and deployment.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleComparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams
    typeJournal Paper
    journal volume144
    journal issue10
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4054723
    journal fristpage104501-1
    journal lastpage104501-6
    page6
    treeJournal of Mechanical Design:;2022:;volume( 144 ):;issue: 010
    contenttypeFulltext
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