Abstract

Information and operations management in libraries presents a unique opportunity to provide insights for the sharing economy. Libraries correspond to a special type of sharing goods, named common-pool resources. Such resources have two characteristics: they are non-exclusive, but rival to each other. Service operations in libraries involve thousands of operations every year, making them a perfect context for the use of big data analytics capabilities (BDAC) to provide real-world evidence on the potential existing challenges in the sharing economy. Employing a novel dataset related to 723,798 library transactions, made by 16,232 individual users during a 10-year period (2006–2015), we estimate peer effects among users via regression analysis, considering the number of books each user borrows. Our main results suggest that a rise in the number of loans among a user’s peer group correlates with her own loans, an evidence of positive peer effects. However, a closer look at the data suggests a high degree of heterogeneity, in terms of behavioral patterns. First, we suggest that peer effects do not occur in the case of users who are not subject to monetary fines. Second, peer effects vary according to users’ category (student or non-student), and area of study (management, accounting, economics, and other courses). Third, there is evidence of different magnitudes of peer effects according to time in school, which suggests the existence of learning effects in a library setting. The results reported in this paper highlight the important role of big data analytics capabilities to uncover new challenges of the sharing economy, having important implications, both in theoretical and practical terms.