Reimagining Theories and Methods to Understand and Enable the Algorithmically Infused Changing Nature of Work
Professor Noshir Contractor

ABSTRACT

Computational social science offers the potential to reimagine communication theories and methods to understand and enable the algorithmically infused changing nature of work. Researchers have heralded for decades the potential of social network analysis to focus not only on who people are, but also who they know. Social network analysis has been used to identify “high potentials,” who has good ideas, who is influential, what teams will get work done efficiently and effectively is well established based on decades of research. The challenge has been the collection of network data via surveys that are time consuming, elicit low response rates and have a high obsolescence. This talk presents empirical examples ranging from corporate enterprises to simulated long duration space exploration to demonstrate how we can mine “digital exhaust”— data created by individuals every day in their algorithmically infused digital transactions, such as recommendations, newsfeeds, chats, “likes,” “follows,” @mentions, and file collaboration — to address challenges they face with issues such as team conflict, team assembly, diversity and inclusion, succession planning, and post-merger integration.