The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries |
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| Institution: | 1. Fundação Escola de Comércio Álvares Penteado, Campus Liberdade - Avenida da Liberdade, 532 - Liberdade, São Paulo - S.P., 01502-001, Brazil;2. Montpellier Business School, 2300, Avenue Des Moulins, Montpellier – Cédex 4, 34185, France;1. Department of Industrial Systems and Engineering, Indian Institute of Technology-Kharagpur, Kharagpur, 721302, India;2. Reliability Engineering Centre, Indian Institute of Technology-Kharagpur, Kharagpur 721302, India;1. Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy;2. Department of Enterprise Engineering, University of Rome Tor Vergata, Via Orazio Raimondo, 00173, Rome, Italy;1. Department of Business and Law, University of Calabria, Italy;2. Department of Economics and Statistics, University of Naples Federico II, Italy;1. University of Perugia, Department of Engineering, Via G. Duranti, 93, 06125, Perugia, Italy;2. MIT Center for Collective Intelligence, 245 First Street, 02142, Cambridge, MA, USA;3. University of Rome Tor Vergata, Department of Enterprise Engineering, Via del Politecnico 1, 00133, Rome, Italy |
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| 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. |
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| Keywords: | Big data analytics capabilities Information and operations management Libraries Peer effects Sharing economy |
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