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This study aims to investigate the factors that may hinder or facilitate consumers’ adoption of mobile-commerce (m-commerce) activities in the context of developing countries exemplified here by Oman.

A conceptual model was developed through integrating factors from UTAUT2 (performance expectancy, expectancy effort, social influence, facilitating conditions, hedonic motivation, price value, habit and self-efficacy) and SERVQUAL (system quality, service quality and information quality). Data were collected from 530 Omani m-commerce users through a cross-sectional survey.

The results of the structural equation modelling showed that consumers’ behavioural intention (BI) towards m-commerce adoption was significantly influenced by information quality, habit, performance expectancy, trust, hedonic motivation, service quality, price value and facilitating conditions, in their order of influencing strength, and explained 65.5 per cent of the variance in BI. Unexpectedly, effort expectancy, social influence, self-efficacy and system quality had no significant effect on BI.

This study will explain the currently relatively low penetration rate of m-commerce adoption in Oman, which will help local m-commerce businesses to develop the right organizational strategies, especially related to marketing strategies and developing mobile applications, which will draw the attention of many users.

This is one of the few studies that integrates UTAUT2 with SERVQUAL and tests the proposed model in non-Western cultural contexts. Specifically, in contrast to previous studies, diversity of individuals’ acceptance behaviour is examined in Oman.

The purpose of this paper is to investigate the interrelationships among knowledge management (KM), information systems (IS) and employees’ empowerment (EE) on employees’ performance (EP).

Accordingly, a structural model is developed that delineates the interactions among these constructs and explores the mediating effect of EE on the relationship between KM, IS and EP. A questionnaire-based survey was designed to test the aforementioned model based on dataset of 287 employees’ pharmaceutical industries in Jordan. The model and posited hypotheses were tested using structural equation modeling analysis.

The results indicated that KM and IS positively and significantly affect EE, in which the latter impact EP as well. However, neither KM nor IS proved to be positively related to EP. Additionally, EE positively and significantly mediated the relationship between KM and EP, besides the relationship between IS and EP.

This is one of the few studies which investigate the interrelationships among KM, IS and EE on EP, and the first to test the model on companies in the pharmaceutical industries in Jordan.

This paper aims to investigate the perceptions of Omani citizens toward the use of social media by the government for participatory and interactive relationships. More precisely, the descriptive nature of the study resides in its ability to explain how social media users regard the current status and levels of presence, transparency, engagement, responsiveness and trust about the current use of social media by the Omani Government.

A quantitative approach was used to collect data. This was done via a self-administered questionnaire from a return sample of 1,769 citizens drawn from different places in Oman. These citizens were considered as well-informed and regular active users of social media. The reviewed literature provided a basis for the construct of the questionnaire.

The overall results indicated modest levels of agreement in all of the investigated factors. The neutral findings suggest that there is a level of uncertainty among the respondents regarding how the government is determining the potential of social media for participatory and interactive relationships. Findings in this study advocate the outcomes of the recent Arab Social Media Report, plus the few relevant studies included in the literature, which nearly stated that although there is a growing use of social media among citizens, governments are failing to take full advantage of social media. Governments are also failing to engage citizens to design and deliver more efficient and collaborative services, per this study’s findings.

The findings call for the importance of strategically framing the use of participatory social media by the government. In a broader sense, the findings of this study are beneficial to all contexts that share similar political and socio-economic philosophy, especially the Arab states and most of the developing countries. The findings provide insights for governments in need of developing social media strategies to promote more collaborative and interactive governance.

The study aids in understanding the views of citizens who are the current major players in a highly technology-driven environment. This environment is found to be transforming the relationship between citizens and governments. The study adds knowledge to the currently scarce body of literature dealing with issues pertaining to citizen–government relationships in social media in the Arab states, and similar contexts in developing countries. Its findings may provide valuable insights for policy makers to leverage collaborative relationships between governments and citizens.

One of the most important Information Security (IS) concerns nowadays is data theft or data leakage. To mitigate this type of risk, organisations use a solid infrastructure and deploy multiple layers of security protection technology and protocols such as firewalls, VPNs and IPsec VPN. However, these technologies do not guarantee data protection, and especially from insiders. Insider threat is a critical risk that can cause harm to the organisation through data theft. The main purpose of this study was to investigate and identify the threats related to data theft caused by insiders in organisations and explore the efforts made by them to control data leakage.

The study proposed a conceptual model to protect organisations’ data by preventing data theft by malicious insiders. The researchers conducted a comprehensive literature review to achieve the objectives of this study. The collection of the data for this study is based on earlier studies conducted by several researchers from January 2011 to December 2020. All the selected literature is from journal articles, conference articles and conference proceedings using various databases.

The study revealed three main findings: first, the main risks inherent in data theft are financial fraud, intellectual property theft, and sabotage of IT infrastructure. Second, there are still some organisations that are not considering data theft by insiders as being a severe risk that should be well controlled. Lastly, the main factors motivating the insiders to perform data leakage activities are financial gain, lack of fairness and justice in the workplace, the psychology or characteristics of the insiders, new technologies, lack of education and awareness and lack of management tools for understanding insider threats.

The study provides a holistic view of data theft by insiders, focusing on the problem from an organisational point of view. Organisations can therefore take into consideration our recommendations to reduce the risks of data leakage by their employees.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

This paper aims to explore the issues of information technology (IT) disaster recovery (DR) and business continuity planning (BCP) in light of Cyclone Gonu in Oman.

The paper includes a survey of the latest literature on the subject, then documents a study of public and private sector organizations together with their DR and BCP practices.

The paper investigates how public and private organizations in Oman plan to respond to disasters. It shows that while some organizations pay attention to the need for DR/BCP, many do not. A significant finding is that while organizations have disaster related plans, almost half of those surveyed do not rehearse them. Nevertheless, organizations surveyed indicate that they have learned valuable lessons from Gonu. It remains to be seen whether these lessons will be turned into effective and properly deployed DR/BCP plans.

This paper draws lessons from the experiences and challenges raised by Gonu, and concludes with a set of recommendations that organizations may adopt to ensure business continuity. It provides a useful evaluation of the preparedness of IT departments in both public and private sectors in Oman. The recommendations given at the end of the paper could be of a great value for many organizations and groups, spreading awareness of the importance of being prepared for such eventualities.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The purpose of this paper is to propose a site selection method using grey system theory for a desalination plant in Libya.

In order to tackle incompleteness and imprecision of human’s judgments, grey numbers were used. This work uses a grey-based approach to represent decision makers’ comparison judgments and extent analysis method to select the best site. Therefore, a real case study of a selection problem of a site selection of desalination plant in Libya was used to illustrate the proposed approach.

Site selection in a desalination plant can be one of the most important decisions in planning a desalination project. The decision affects both the project cost and potentially the project schedule. Based on the results of grey model, a clear order of these sites and the degree of preference are obtained. This paper presents a way to improve a site selection by using a grey model, especially in a complex environment like Libya.

To the best knowledge of the authors, there is no literature for site selection using grey system theory in a desalination plant in Libya. This attempt may well enhance and facilitate the decision-making process of the best site in the country involved in this research.

The purpose of this paper is to propose a supplier selection method using grey system theory for a steelmaking company in Libya.

In order to tackle incompleteness and imprecision of human’s judgments, grey numbers were used. This work uses a grey-based approach to represent decision makers’ comparison judgments and extent analysis method to select the best supplier. Therefore, an example of a selection problem of a steelmaking company in Libya was used to illustrate the proposed approach.

Supplier selection in a supply chain is a critical strategic decision for company’s success and has attracted much attention of both academic scholars and decision makers. The authors have found that the Grey model can play an important role in improving supplier selection strategy, especially when it is in a situation where complex sustainability environments (i.e. Libya) exist.

No literature has been found till date for selection of supplier using grey system theory in a steelmaking company in Libya. An attempt in this regard could enhance a decision-making technique for selecting the best suppliers for the selected case company.

Performance evaluation of airports or any other organisation is paramount for improving performance. The purpose of this paper is to evaluate and compare the performance of the three international airports in Libya (MJI, MRA, and LAQ airports) by considering five aspects of performance.

The considered aspects are airport service quality, airport operations, airport economy, safety and security, and environmental. The paper uses the grey system theory to assess these airports by summarizing the opinions of experts.

The finding of this study provides directions of the evaluated airports to take the correct actions to improve overall performance.

No literature has been found till date is to evaluate and compare the performance of the international airports in Libya.