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Keywords = sociometer

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14 pages, 530 KiB  
Article
Silence as a Quiet Strategy: Understanding the Consequences of Workplace Ostracism Through the Lens of Sociometer Theory
by Jun Yang, Bin Wang, Yijing Liao, Feifan Yang and Jing Qian
Behav. Sci. 2025, 15(8), 1022; https://doi.org/10.3390/bs15081022 - 28 Jul 2025
Abstract
Existing research has predominantly framed defensive silence as an avoidance response to interpersonal mistreatments. Moving beyond this view, this study theorizes defensive silence as a proactive strategy for managing interpersonal relationships through the lens of sociometer theory. We posit that workplace ostracism will [...] Read more.
Existing research has predominantly framed defensive silence as an avoidance response to interpersonal mistreatments. Moving beyond this view, this study theorizes defensive silence as a proactive strategy for managing interpersonal relationships through the lens of sociometer theory. We posit that workplace ostracism will reduce employees’ organization-based self-esteem (OBSE), which in turn increases their subsequent defensive silence to avert further damage to relationships. In addition, we also expect a moderating role of the sense of power in mitigating the negative impact of workplace ostracism on OBSE. Based on the multi-wave, multi-source data of 345 employees and their 82 immediate supervisors, we tested all the hypotheses. Results from multilevel modeling indicated that OBSE mediated the indirect effect of workplace ostracism on defensive silence, and also supported the moderation role of sense of power. Our theoretical model provides a novel perspective that deepens the understanding of defensive silence and suggests implications for managerial practices. Full article
(This article belongs to the Section Organizational Behaviors)
20 pages, 2866 KiB  
Article
Discovering and Understanding City Events with Big Data: The Case of Rome
by Barbara Furletti, Roberto Trasarti, Paolo Cintia and Lorenzo Gabrielli
Information 2017, 8(3), 74; https://doi.org/10.3390/info8030074 - 27 Jun 2017
Cited by 14 | Viewed by 5627
Abstract
The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in [...] Read more.
The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists. Full article
(This article belongs to the Special Issue Big Data Analytics and Data-Driven Science)
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