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Mobile e-commerce has brought convenience to consumers. But for goods such as shoes, mobile e-commerce has failed to provide the same experience as consumers would have in physical stores, and that also causes problems for online merchants, such as high return rates. As a result, the augmented reality (AR) virtual shoe-try-on function appeared. The way that AR virtual shoe-try-on study different from other AR virtual try-on studies is that AR virtual shoe-try-on study only satisfies consumers' visual experience and consumers cannot judge whether the shoes are comfort or not. Whether consumers would accept AR virtual try-on function to help them make purchase decision due to the visual experience provided by AR virtual try-on function is worth discussion. Measuring users' perceptions and preferences can help companies design AR shoe-trying functions and provide services more cost-effectively.

To promote the continuous use and better development of such mobile e-commerce based on the technology acceptance model (TAM), this study explored the influencing factors for users' intentions to continue using the AR virtual shoe-try-on function, including the perceived usefulness, perceived ease of use, system quality, perceived playfulness and attitude.

The results of this study showed that TAM is a powerful theoretical tool of the new technology in mobile e-commerce and that the system quality and perceived playfulness also have a positive impact on the original variables of TAM. System quality and perceived playfulness are important predictors of users' continuance intentions to use the AR virtual shoe-try-on function.

The main contribution of this study to model iteration and theoretical update is to verify the applicability of the TAM in the AR shoe-try-on function and to expand TAM model with system quality and perceived playfulness. The authors' results will help shoe enterprises win users' recognition through AR shoe-try-on function and improve users' continuance intention of use.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.