AJIS Article Release: Theorizing E-Commerce Business Models: On the Impact of Partially and Fully Supported Transaction Phases on Customer Satisfaction and Loyalty

Madlberger, M., & Matook, S. (2017). Theorizing E-Commerce Business Models: On the Impact of Partially and Fully Supported Transaction Phases on Customer Satisfaction and Loyalty. Australasian Journal of Information Systems, 21. doi: http://dx.doi.org/10.3127/ajis.v21i0.1426

Abstract

Although considerable research has been conducted on the definition and classification of e-commerce business models, little research has integrated the support and impact of distinct transaction phases within e-commerce business models. Indeed, any channel design decisions depend on the arrangement of the three transaction phases, viz. information phase, agreement phase and fulfilment phase. Whereas in literature, a complete support of transaction phases is implicitly assumed, in practice, e-commerce firms exist that consciously do not support all three transaction phases through the online channel. In cases for which only certain phases are supported in one channel, the customer is required to switch to another channel to complete the transaction. To gain an initial understanding of the relevance of transaction phases, we examine the influence of transaction phase support in one channel on satisfaction and reuse intention of an e-commerce website under consideration of users’ prior experiences. We conduct a laboratory experiment involving two e-commerce business models that differ only in the number of supported transaction phases via the online channel. Empirical data was analysed using a Bayesian network approach. Our analysis indicates the types of users who value partial support compared to those users who prefer full support of transaction phases. The results will assist firms in meeting future challenges regarding user engagement and attraction to their online stores.

Keywords Electronic commerce business model; transaction phases; channel design; channel switching; customer satisfaction; reuse intention; tourism industry; Bayesian network