Electronic government (E-Government) is the use of information and communication technology by the government to increase the service to citizens. E-government also could be applied to the legislative and judicative to improve internal efficiency of democratic governance. However, technological, governing and social issues have to tread carefully in order to adopt these phenomena. This study aims to find critical factor that influences e-government adoption. Furthermore, comprehensive analysis base on the bibliometric technic on various resources has been chosen to guide this work. Several dependent variables such as information quality, trust, and system quality also considered relevant were integrated with the unified theory of acceptance and use of technology (UTAUT) constructs as examining variables affecting the adoption of e-government. Finally, this study found a formulation of the conceptual framework on the basis of existing experience and their relationship.

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Constructing a prediction model of machining performance is useful to improve its process efficiency. Artificial neural network (ANN) has been widely used in prediction works, capable of solving complex problems with numerous parameters. The present study aims to describe the application of the ANN technique in predicting the machining performance of a natural material. Bovine horns were the selected natural materials. Bovine horns are sustainable, recyclable, and abundant source for industrial applications. The outputs of the predictive model were surface roughness and energy consumption, whereas the input data were spindle speed, depth of cut and feed rate of a face milling. It was found that the ANN-based prediction model of bovine horns produced a high accuracy prediction (95.4%). The outcome of this study may be referred by similar studies on other natural materials, supporting the global efforts in improving the industrialization of natural materials.