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E-book adoption behaviors through an online sharing platform : A multi-relational network perspective

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The purpose of this paper is to construct a multi-relational network for an online sharing platform in the age of the sharing economy, to identify the factors impacting users’ product adoption behavior and to predict consumers’ purchases of user-generated products on the platform.


The study conducted multi-relational network analyses of five different sub-networks in identifying influential factors for e-book adoption. Meanwhile, the study adopted machine learning methods with different classification algorithms and feature sets to predict users’ purchasing behaviors.


The authors found that an individual’s adoption of a product was correlated with his or her purchasing habits and collaboration with others on the online sharing platform. Through the inclusion of network features, the authors were able to build a predictive model that forecasted consumers’ purchases of user-generated e-books with reasonable accuracy.

Research limitations/implications

The interdisciplinary approach used in the study can serve as a good reference for identifying factors impacting the product adoption behavior of users in the online sharing platform, through employing different sociological and computational methods.

Practical implications

The outcome of the study has provided important managerial implications, especially for the design of social commerce platform in the age of the sharing economy.

Social implications

The authors verified the social influence impacting consumers’ product adoption behavior and shed light on the value of collaboration in the age of the sharing economy.


The study was the first to identify user-generated e-book adoption on an online sharing platform from a multi-relational network perspective. The idea and the approach supplied a new method of behavioral analysis in the context of a sharing economy.
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Keywords: E-book adoption; Machine learning; Multi-relational network; Online sharing platform; Predictive model; Social commerce

Document Type: Research Article

Affiliations: 1: School of Information, Central University of Finance and Economics, Beijing, China 2: Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA

Publication date: June 18, 2020

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