JOURNAL ARTICLE
Navigating Virtual Connections: Exploring the Impact of Workplace Friendship on Team Performance During the COVID-19 Pandemic.
Published In: Journal of Information & Knowledge Management, 2024, v. 23, n. 2. P. 1 1 of 3
Database: The Belt and Road Initiative Reference Source 2 of 3
Authored By: Firoz, Mariya; Ghosh, Koustab; Sharma, Dheeraj 3 of 3
Abstract
This study aims to investigate the influence of virtual work friendships on knowledge sharing and team performance in the context of the COVID-19 pandemic. Additionally, it explores whether perceived task interdependence moderates this relationship. A survey-based research design was employed to collect data from 213 individuals working in IT-based firms. Data were collected in two time lags to capture the dynamics of virtual work friendships, knowledge sharing and team performance. The study reveals a positive and significant relationship between virtual work friendships and team performance. Furthermore, knowledge sharing acts as a mediator between virtual friendships and team performance. Managers should actively promote friendship among employees to enhance virtual work environments. Additionally, when task interdependence is perceived highly, leveraging social resources both virtually and physically can further enhance team performance. This research contributes to the social capital theory by examining the effects of virtual work friendships on team performance in the modern workplace. It is the first study to investigate the moderating role of task interdependence in this context. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Information & Knowledge Management. 2024/04, Vol. 23, Issue 2, p1
- Document Type:Article
- Subject Area:Economics
- Publication Date:2024
- ISSN:0219-6492
- DOI:10.1142/S0219649223500685
- Accession Number:177204721
- Copyright Statement:Copyright of Journal of Information & Knowledge Management is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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