A firm and individual characteristic-based prediction model for E2.0 continuance adoption

Abstract

Enterprise-level 2.0 applications (E2.0) built on cloud computing Web 2.0 infrastructure offer promising new business models. However, recent studies show that most E2.0 firms experience a low free-to-paid conversion rate. Based on accumulated archival data and literature on predictive models and data mining, in this paper, we develop a logit model to predict the likelihood of E2.0 user continuance. The proposed model includes firm-specific and individual characteristics and estimates coefficients relating predictor variables to E2.0 continuance decisions. The sample includes information on 575 paid customers (i.e. firms) with 65,407 individual users and 2,286 previous customers with 99,807 individual users from 2011-2016. The resulting model can help business managers of E2.0 service providers to identify effectively reliable customers, optimize their sales efforts, and increase the free-to-paid conversion rate.