Electronic government (e-government) implementation has played a key role in developing government business processes in order to improve efficiency in data processing, as well as achieving transparency and accuracy. However, although there are benefits of e-government implementation, there is the problem of employees' resistance to change, as people tend to resist any kind of new system. This research was conducted for the purpose of examining the effect of employees' resistance to change including resistance to modern technology, fear of loss of control, feelings of insecurity, fear of inability to implement change, and fear of workload increasing, on implementing an e-government system represented in performance dimensions (efficiency and effectiveness) in Egypt. This paper reflects the lack of research into resistance to change in the specific context of e-government implementation in Egypt. We surveyed 400 respondents in several public organizations in Egypt to investigate the impact of such resistance, in order to determine which behaviors can affect any of the implemented e-government dimensions. The study also determines the differences between employees' responses to implementing the new system depending on variables such as gender, age, educational level, job position, and level of experience. The results reveal that fear of loss of control and fear of workload increasing affect all dependent variables significantly, while feelings of insecurity does not affect efficiency significantly. Resistance to modern technology and a fear of inability to implement change do not have significant effects on the dependent variables.

Nowadays enterprise resource planning (ERP) systems are crucial for each company to automate and manage its business seamlessly and possess a competitive advantage.This paper was conducted with the purpose of determining which ERP capabilities categorized in IT capabilities, including integration, information access, IT skills and agility, and organizational capabilities including qualified human resources, top management support can affect decision-making efficiency and competitive advantage types (low cost advantage and/or differentiation advantage), it also examines the impact of decision-making efficiency on competitive advantage types. The problem of this paper is summarized in the uncertainty of the relationship between ERP capabilities and competitive advantage, as well as the absence of studies in this field. So, a survey was applied on 162 companies in Dakahlia Governorate, Egypt in order to investigates the impact of ERP capabilities on low-cost advantage, differentiation advantage, and decision making efficiency, in order to determine whether each category of ERP capabilities can affect any competitive advantage type and decision-making efficiency in itself or they must be combined with each other to occur impact, and finally examine the decision making efficiency impact on competitive advantage types.

ARTICLE INFO Digitaltransformation is gradually transforming governments and businesses, and making them more competitive, as well as it offers several opportunities for economic growth and prosperity, as it enables countries to including, more diversified educational opportunities, universal access to the internet and a comprehensive and helpful environment to the development process. This paper discusses highlights the concept of the sustainable development and how the Communication and Information Technology industry contributes to achieving the sustainable development; therefore, this paper reviews the 17 SDGs (sustainable development goals) and explains how information technology has affect each goal and then it displays the endeavors of Egypt's government to utilize ICT in achieving sustainable development. These endeavors are represented in the digital projects and initiatives that were launched by the "Ministry of communication and information technology" in collaboration with other partners whether government entities or the private sector.

Nowadays, the acceleration toward digital transformation changes both the way business is done and customers behavior; so hotels have to keep pace with this rapid evolution and seek to digital transformation by adopting the advanced information technologies. This research was conducted with the aim of examining the effect of digital transformation on customer satisfaction so the research focuses on the most prominent information technologies related to the guest-cycle stages. The problem of this paper is summarized in the absence of the related literature in this subject concerning to the Egyptian hotels, as well as the lack of hotel managers and employees' awareness of the optimal utilization of information technologies, in addition to the shortage of the implemented advanced ITs in hotels. So, a survey was applied on 400 respondents in four and five-star hotel guests in three Egyptian cities, Cairo, Sharm-Elshiekh and Hurghada, to investigate the impact of guest-cycle ITs on customer-satisfaction in order to determine which set of ITs in the guest-cycle stages can affect the customers satisfaction, as well as determine the differences between guests perceptions toward the study variables and the differences between their preferences toward using ITs according to their demographic features including; nationality, age and gender. The results revealed the importance of all guest-cycle ITs for customer satisfaction, as each of them has a significant effect on customer satisfaction. As well as, the guests' preferences toward using ITs differ only according to their nationality.

Customer churn prediction recently is one of the vital issues that confronts diverse business industries to sustain the customers base and profits. On the other hand, data scientists employ gigantic customer data to automate the data modelling process to offer these models as a generally portable service. This research has two main contributions: deep learning customer churn prediction model and smart evaluation prediction model service. So, this service harnesses any customer data to automate building, evaluation, and deployment of the churn prediction model. The research consists of three main parts. Firstly, it illustrates the dataset labelling which annotates customers data into churn or non-churn. Secondly, the deep learning churn prediction framework using convolutional neural network (CNN) algorithm. Finally, a case study is presented to show how churn prediction service is automatically trained and generated based on real customer data, where CNN parameters are adapted to achieve the most reliable performance in line with customers' behavior. The applied case study achieves accuracy 0.77, area under the curve (AUC) 0.84 and f1 score 0.83.