A Finite Mixture Logit Model to Segment and Predict Electronic Payments System Adoption

• 成果类型: Article
• 署名作者: Bapna, Ravi; Goes, Paulo; Wei, Kwok Kee; Zhang, Zhongju
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; University of Arizona; City University of Hong Kong; University of Connecticut
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.1090.0277
• 发表日期: 2011
• 页码: 118-133
• 关键词: computer self-efficacy; perceived ease; innovation characteristics; network externalities; technology adoption; user acceptance; EDI ADOPTION; determinants; CHOICE; Heterogeneity

摘要

Despite much hype about electronic payments systems (EPSs), a 2004 survey establishes that close to 80% of between-business payments are still made using paper-based formats. We present a finite mixture logit model to predict likelihood of EPS adoption in business-to-business (B2B) settings. Our model simultaneously classifies firms into homogeneous segments based on firm-specific characteristics and estimates the model's coefficients relating predictor variables to EPS adoption decisions for each respective segment. While such models are increasingly making their presence felt in the marketing literature, we demonstrate their applicability to traditional information systems (IS) problems such as technology adoption. Using the finite mixture approach, we predict the likelihood of EPS adoption using a unique data set from a Fortune 100 company. We compare the finite mixture model with a variety of traditional approaches. We find that the finite mixture model fits the data better, controlling for the number of parameters estimated; that our explicit model-based segmentation leads to a better delineation of segments; and that it significantly improves the predictive accuracy in holdout samples. Practically, the proposed methodology can help business managers develop actionable segment-specific strategies for increasing EPS adoption by their business partners. We discuss how the methodology is potentially applicable to a wide variety of IS research.