To Share or Not to Share

Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Journal Article (2024)
Author(s)

Geoff Musick (Clemson University)

Wen Jie Duan (Clemson University)

Shabnam Najafian (TU Delft - Web Information Systems)

Subhasree Sengupta (Clemson University)

Christopher Flathmann (Clemson University)

Bart Knijnenburg (Clemson University)

Nathan McNeese (Clemson University)

Research Group
Web Information Systems
Copyright
© 2024 Geoff Musick, Wen Duan, S. Najafian, Subhasree Sengupta, Christopher Flathmann, Bart Knijnenburg, Nathan McNeese
DOI related publication
https://doi.org/10.1145/3633074
More Info
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Publication Year
2024
Language
English
Copyright
© 2024 Geoff Musick, Wen Duan, S. Najafian, Subhasree Sengupta, Christopher Flathmann, Bart Knijnenburg, Nathan McNeese
Research Group
Web Information Systems
Issue number
1
Volume number
8
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Abstract

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other’s working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality-derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.