In comparison with the rapid adoption and growth of mobile technologies worldwide, mobile payment services are being adopted at a slower pace than expected. In the literature, most studies about customers’ use of mobile payment have focused mainly on the technological aspects. However, the businesses that provide mobile payment in terms of an ecosystem include various industries. This study integrated and empirically investigated the factors in multi-faceted nature of the mobile payment ecosystems, which influence customers’ acceptance and actual adoption. Although technological and social elements influence customers’ intention to use mobile payment for transactions, this study verified the important connection between cognition (intention to use) and behavior (actual usage). Service quality, service innovation, brand equity, switching costs, and public policy all impact the gap between intention and actual adoption of mobile payment.

Currently, the green procurement activities of private hospitals in Taiwan follow the self-built green electronic-procurement (e-procurement) system. This requires professional personnel to take the time to regularly update the green specification and software and hardware of the e-procurement system, and the information system maintenance cost is high. In the case of a green e-procurement system crash, the efficiency of green procurement activities for hospitals is affected. If the green e-procurement can be moved to a convenient and trusty cloud computing model, this will enhance the efficiency of procurement activities and reduce the information maintenance cost for private hospitals. However, implementing a cloud model is an issue of technology innovation application and the technology-organization-environment (TOE) framework has been widely applied as the theoretical framework in technology innovation application. In addition, finding the weight of factors is a multi-criteria decision-making (MCDM) issue. Therefore, the present study first collected factors influencing implementation of the cloud mode together with the TOE as the theoretical framework, by reviewing the literature. Therefore, an expert questionnaire was designed and distributed to top managers of 20 private hospitals in southern Taiwan. The fuzzy analysis hierarchical process (FAHP), which is a MCDM tool, finds the weights of the factors influencing private hospitals in southern Taiwan when they implement a cloud green e-procurement system. The research results can enable private hospitals to successfully implement a green e-procurement system through a cloud model by optimizing resource allocation according to the weight of each factor. In addition, the results of this research can help cloud service providers of green e-procurement understand users’ needs and develop relevant cloud solutions and marketing strategies.

This article builds a systematic and reliable site selection prediction model for a chain of convenience stores (CVSs) to improve the existing decision method of using experienced managers to select sites. Specifically, this study used an artificial neural network (ANN) technique—back-propagation neural network (BPN)—to build the prediction model. To achieve optimization in executing the BPN, the Taguchi method (TM) was adopted to find the optimal parameters for the BPN. The actual data from a chain of CVSs was employed to validate the model. The results indicated that the prediction accuracy rate and decision quality of the proposed model were higher than those of the existing manager-directed decision method. With intense retail competition, the accurate determination of the location of a new convenience store (CVS) is vital to its success. This study asserts that with systematic and scientific assessment, the site selection decision for new chain CVSs will be less human-biased in nature if the prediction model is used as an auxiliary decision-making tool.