Search for: All records

Abstract Shifts to hybrid work prompted by the COVID-19 pandemic have the potential to substantially impact social relationships at work. Hybrid employees rely heavily on digital collaboration technologies to communicate and share information. Therefore, employees’ perceptions of the technologies are critical in shaping organizational networks. However, the dyadic-level misalignment in these perceptions may lead to relationship dissolution. To explore the social network consequences of hybrid work, we conducted a two-wave survey in a department of an industrial manufacturing firm (N = 169). Our results show that advice seekers were less likely to maintain their advice-seeking ties when they had a mismatch in ease-of-use perceptions of technology with their advisors. The effect was more substantial when advice seekers spent more time working remotely. The study provides empirical insights into how congruence in employees’ perceptions of organizational communication technologies affects how they maintain advice networks during hybrid work. 

This inquiry is guided by a curiosity around the stories that teachers tell about their students, content, and pedagogical approaches focused on data and computational literacies. We present a form of storytelling with theory as we apply theories of syncretism and translanguaging to empirical vignettes about teachers’ sensemaking. We also present a form of storytelling of theory, drawing on teachers’ stories to help us better understand how these theories are related to each other. We bring two teachers’ stories into conversation: one from the Writing Data Stories (WDS) project and the other from the Participating in Literacies and Computer Science (PiLa-CS) project. Both projects utilized translanguaging and syncretism in their conceptions and designs, working with teachers to design for expansive forms of data-based and computational literacies. 

Abstract Despite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to measure team-level expertise diversity by determining similarity or dissimilarity in members’ prior knowledge and skills. Using this measure on 2.8 million team invented patents granted by the US Patent Office, we show evidence of trends in expertise diversity over time and across team sizes, as well as its relationship with the quality and impact of a team’s innovation output.