research-article

Open access

Knowing Unknown Teammates: Exploring Anonymity and Explanations in a Teammate Information-Sharing Recommender System

Authors:

Elizabeth S. Gilman,

Nathan J. McNeese,

Bart Knijnenburg,

Thomas O'NeillAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 7, Issue CSCW2

Article No.: 284, Pages 1 - 34

https://doi.org/10.1145/3610075

Published: 04 October 2023 Publication History

Abstract

A growing organizational trend is to utilize ad-hoc team formation which allows for teams to intentionally form based on the member skills required to accomplish a specific task. Due to the unfamiliar nature of these teams, teammates are often limited by their understanding of one another (e.g., teammate preferences, tendencies, attitudes) which limits the team's functioning and efficiency. This study conceptualizes and investigates the use of a teammate information-sharing recommender system which selectively shares interpersonal recommendations between unfamiliar teammates (e.g., "Your voice may be overshadowed by this teammate when making decisions...") to promote teammate understanding. Through a mixed-methods approach involving 105 participants working on actual unfamiliar teams, this study explores how presentation elements such as anonymity and explanations influence system perceptions and how anonymity influences team outcomes. Results indicate that anonymizing recommendations was associated with worse team measures, particularly team satisfaction and team cohesion. Qualitative results shed light on why team members perceived privacy concerns and team benefits associated with using the system. We contribute to CSCW through a better understanding of how to support unfamiliar teams, the conceptualization and empirical investigation of a novel teammate information-sharing recommender system, and foundational design recommendations associated with such a system.

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Cited By

Musick GDuan WNajafian SSengupta SFlathmann CKnijnenburg BMcNeese N(2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1145/3633074
Saifudin IWidiyaningtyas T(2024)Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, DatasetsIEEE Access10.1109/ACCESS.2024.335927412(19827-19847)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3359274

Index Terms

Knowing Unknown Teammates: Exploring Anonymity and Explanations in a Teammate Information-Sharing Recommender System
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 7, Issue CSCW2

CSCW

October 2023

4055 pages

EISSN:2573-0142

DOI:10.1145/3626953

Editor:
Jeff Nichols
Apple Inc., United States

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Copyright © 2023 ACM.

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Published: 04 October 2023

Published in PACMHCI Volume 7, Issue CSCW2

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Cited By

Musick GDuan WNajafian SSengupta SFlathmann CKnijnenburg BMcNeese N(2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1145/3633074
Saifudin IWidiyaningtyas T(2024)Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, DatasetsIEEE Access10.1109/ACCESS.2024.335927412(19827-19847)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3359274

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