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Purpose The purpose of this paper is to offer insights into the development of eLearning systems and the perceptions of key players in the management of eLearning systems in three large universities in Saudi Arabia. It establishes the relative importance of different factors and compares these findings with studies conducted elsewhere in the world. Design/methodology/approach Desk research was conducted to gather a profile of the eLearning initiatives in the participating universities. Structured interviews were conducted with senior managers with responsibility for implementing and promoting eLearning in their universities. The interview protocol prompted discussion of the importance of the following sets of factors in the success and acceptance of eLearning: student characteristics, instructor characteristics, learning environment, instructional design, and support. Interviews were transcribed and analysed using thematic analysis. Findings Supported by the Saudi Government, the three universities in this study have been developing their eLearning services. The two most important groups of critical success factors in this process were regarded as those related to student and instructor characteristics. Further analysis within each group of factors suggested that participants regarded instructor knowledge with learning technologies and student knowledge of computer systems, and technical infrastructure as important facilitators of success. Amongst instructional design factors, clarity of learning objectives and content quality were regarded as important. Insights are offered as to the reasons for these selections. Originality/value This study furthers earlier research on eLearning managers’ views and contributes to understanding of eLearning and its management in the Middle East.

Knowledge sharing strengthens individual creativity, critical thinking and innovation. It also improves research and development endeavors, performance and productivity at the organizational level. Knowledge sharing decreases the amount of red-tape faced by organizations, firms and individuals in achieving economic, as well as social improvements. Notwithstanding the plethora of empirical studies on knowledge sharing determinants, much confusion has been produced by differing conclusions. Using original data collected via questionnaires from a sample of 404 participants at a Saudi public university, this analysis tested the effect of social capital on knowledge sharing intentions and behaviors. Findings from the Structural Equation Model found support to the hypotheses claiming positive associations between social ties, trust, identification, reciprocity, shared language, and shared vision and knowledge sharing. This study proposes a practice-based strategy for higher learning institutions to improve knowledge sharing behaviors built on the two components of enhancing enabling environments and technical skills. On the theoretical level, this study argues that the effects of social capital constructs differ with respect to the context considered. In higher education, social capital is thought to have a weak significant positive explanatory power on knowledge sharing behaviors.

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In recent times, computer vision related face image analysis has gained significant attention in various applications namely biometrics, surveillance, security, data retrieval, informatics, etc. The main objective of the facial analysis is to extract facial soft biometrics like expression, identity, age, ethnicity, gender, etc. Of these, ethnicity recognition is considered a hot search topic, a major part of community with deep connections to many social and ecological concerns. The deep learning and machine learning methods is merit for effective ethnicity classification and recognition. This study develops a facial imaging based ethnicity recognition using equilibrium optimizer with machine learning (FIER-EOML) model. The goal of the FIER-EOML technique is to detect and classify different kinds of ethnicities on facial images. To accomplish this, the presented FIER-EOML technique applies an EfficientNet model to generate a set of feature vectors. For ethnicity recognition, the presented model uses long short-term memory method. To improve the recognition performance, the FIER-EOML technique utilizes EO algorithm for hyperparameter tuning process. The performance validation of the FIER-EOML technique is tested on BUPT-GLOBALFACE dataset and the results are examined under several measures. The comprehensive comparison study reported the enhanced performance of the FIER-EOML technique over other recent approaches.