Today, social commerce is one of the most rapidly growing subsectors of e-commerce, creating new opportunities for brands of all types and sizes. However, despite its popularity and potential, social commerce faces significant challenges, including issues of privacy, trust and ethics. This paper sets out to identify key aspects of privacy which influence ongoing user engagement with social commerce, so that social media, and other social commerce, platforms can more effectively address the issue. In particular, the paper seeks to determine the extent to which these aspects of privacy are a function of gender and, therefore, to increase our understanding of the role of gender in determining a user’s likelihood of sustainable engagement with s-commerce. To explore these issues, the study deploys a mixed methodology (semi-structured interviews and questionnaires) to examine the views of a broad demographic of s-commerce users in Saudi Arabia. The results allowed us to identify three distinct aspects of online privacy that significantly influence the likelihood of engaging in s-commerce and also demonstrated that the relative importance of these aspects is a function of gender. The study enhances current understanding of the role of gender in intention to use s-commerce and provides a framework for further research. The findings of the study will be of interest to all parties involved in the design and provision of s-commerce services, including social media platforms.

Today, the concepts of the smart city and smart government are well established, and have become key in dealing with the challenges faced by rapidly increasing urbanisation across the world. One of the many ways in which smart cities and government can help meet these challenges is by empowering citizens to take more and better control of their own decisions, by giving them access to open (i.e., public and unrestricted) data via online portals. These portals can enable citizens and professionals to make more informed decisions across a wide variety of areas, from health and traffic issues to developing new businesses. They can also encourage citizens to become more involved in the making of more effective social policy. Open data is increasingly seen as a key enabler in designing and implementing knowledge-based solutions for more sustainable urban development. For these reasons, as well as others, OGD (Open Government Data) portals are already being implemented in many countries. However, if smart cities are to maximise their potential, OGD portals must be understood, accepted and—crucially—used by the public. The extent to which this is the case is not clear, as there have been few studies that have specifically examined the impact of open data in a smart city context. This is particularly true in the GCC (Gulf Cooperation Council) countries. This research uses a mixed methods (questionnaires and interviews) approach to examine this issue by carrying out a comparative study of the use of portals across the region and, therefore, whether these countries are making optimum use of open data. An existing evaluation framework was used with a group of evaluators representing professional users who are not experienced in using data portals. The findings of the research suggest that these countries are not yet realising the full potential of their portals, and more effective support for the transition to smart cities could be achieved with fuller and better cross-national cooperation.

Autonomous vehicles (AVs) require robust decision-making capabilities to navigate dynamic environments securely and effectively. Furthermore, advanced sensors and computational models are utilized to perceive the environment and predict the behavior of other road users. The main challenges include dealing with unpredictable factors such as weather fluctuations, traffic patterns, and road obstacles. While these technologies provide contextual awareness, existing techniques often face difficulties in handling complex sensor data under rapidly changing conditions and selecting the most useful features for navigation decision classification. To overcome these challenges, there is a need for reliable and intelligent architectures for navigation decision classification systems to support AVs by utilizing AI-driven machine learning (ML) and deep learning (DL) techniques. Currently, DL methods have gained significant importance in enhancing AV capabilities by enabling improved perception and decision classification from complex data. Therefore, this paper introduces an Intelligent Bidirectional Temporal Attention Framework for Navigation Decision Classification in Autonomous Vehicles (IBTAF-NDCAV). The major aim of this research is to design an effective navigation decision classification system for classifying driving actions using sensor data. To accomplish this aim, the proposed method applies improved mutual information-based feature selection to select informative features while reducing redundant ones. For classification, the proposed IBTAF-NDCAV method utilizes a bidirectional temporal attention convolutional network as an AI-driven neural computing model that captures temporal dependencies and effectively classifies navigation decisions. To enhance convergence and training stability, the Adamax optimizer is used to optimize the neural network training process. The experimental result analysis of the IBTAF-NDCAV technique is conducted on a benchmark Autonomous Driving Sensor-Kinematics dataset, which includes 5000 samples distributed across five classes: follow, yield, stop, lane change, and overtake. The comparative results demonstrate superior performance, achieving an accuracy of 96.76%, with class-wise precision, recall, F1-score values and MCC of 90.97%, 81.68, 85.31%, and 83.40% on the 20% testing set. The temporal attention mechanism highlights informative temporal patterns contributing to the navigation decisions, thus providing a basis for interpreting the model’s decision-making procedure. Therefore, the result highlights the effectiveness of the model, making it appropriate for utilization in autonomous vehicle navigation and path-planning applications.