research-article

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LibRA: On LinkedIn based Role Ambiguity and Its Relationship with Wellbeing and Job Performance

Authors:

Manikanta D. Reddy,

Stephen Mattingly,

Anusha Sirigiri,

Munmun De ChoudhuryAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 3, Issue CSCW

Article No.: 137, Pages 1 - 30

https://doi.org/10.1145/3359239

Published: 07 November 2019 Publication History

Abstract

Job roles serve as a boundary between an employee and an organization, and are often considered building blocks in understanding the behavior and functioning of organizational systems. However, a lack of clarity about one's role, that is, one's work responsibilities and degree of authority, can lead to absenteeism, turnover, dissatisfaction, stress, and lower workplace performance. This paper proposes a methodology to quantitatively estimate role ambiguity via unobtrusively gathered data from LinkedIn, shared voluntarily by a cohort of information workers spanning multiple organizations. After successfully validating this LinkedIn based measure of Role Ambiguity, or LibRA against a state-of-the-art gold standard, drawing upon theories in organizational psychology, we examine the efficacy and convergent validity of LibRA in explaining established relationships of role ambiguity with wellbeing and performance measures of individuals. We find that greater LibRA is associated with depleted wellbeing, such as increased heart rate, increased arousal, decreased sleep, and higher stress. In addition, greater LibRA is associated with lower job performance such as decreased organizational citizenship behavior and decreased individual task performance. We discuss how LibRA can help fill gaps in state-of-the-art assessments of role ambiguity, and the potential of this measure in building novel technology-mediated strategies to combat role ambiguity in organizations.

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Index Terms

LibRA: On LinkedIn based Role Ambiguity and Its Relationship with Wellbeing and Job Performance
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media
    2. 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 3, Issue CSCW

November 2019

5026 pages

EISSN:2573-0142

DOI:10.1145/3371885

Editors:
Airi Lampinen
Stockholm University, Sweden
,
Darren Gergle
Northwestern University, USA
,
David A. Shamma
FXPAL, USA

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Published: 07 November 2019

Published in PACMHCI Volume 3, Issue CSCW

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Das Swain VSaha K(2024)Teacher, Trainer, Counsel, Spy: How Generative AI can Bridge or Widen the Gaps in Worker-Centric Digital Phenotyping of WellbeingProceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3663384.3663401(1-13)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3663384.3663401
Das Swain VGao LMondal AAbowd GDe Choudhury M(2024)Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information WorkersProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642716(1-30)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642716
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