Digital Peer Interactions and Knowledge Transfers: Evidence from Online Business Education

Online learning and knowledge exchange have grown tremendously over the past decade and popularity has accelerated further during the recent COVID-19 pandemic. However, most platforms face common challenges of low user engagement and online education suffers from low course completion in addition. As it remains an open puzzle how these frictions can be overcome, we ask if and which social interactions can contribute to efficient knowledge exchange online. Using a unique dataset of 21,000 working professionals enrolled in business skills training courses of an elite U.S. business school over five years, we show a significantly positive relationship between social interactions and learning outcomes. To examine mechanisms, we develop a novel comment classification matrix of individuals’ comments using cutting-edge NLP methods. Our results suggest that there is considerable heterogeneity in the way that learners communicate with one another and, in particular, disagreement as well as receiving ‘likes’ on one’s content, can spark follow-on interactions. Our results have direct implications for fostering improved engagement and performance in online courses. Hence, designers of digital (learning) platforms may want to harvest the power of social interactions better to facilitate knowledge flows and peer-to-peer learning among users.