Mohamed Khalifa

The objective of this study is to investigate the factors affecting online shopping. A model explaining the impact of different factors on online shopping intentions and behavior is developed based on the theory of planned behavior. The model is then tested empirically in a longitudinal study with two surveys. Data collected from 705 consumers indicate that subjective norms, attitude, and beliefs concerning the consequences of online shopping have significant effects on consumers' intentions to buy online. Behavioral control and intentions significantly influenced online shopping behavior. The results also provide strong support for the positive effects of personal innovativeness on attitude and intentions to shop online. The implications of the findings for theory and practice are discussed.

In this study, we further develop the information systems continuance model in the context of online shopping, using a contingency theory that accounts for the roles of online shopping habit and online shopping experience. Specifically, we argue and empirically demonstrate that although conceptually distinct, online shopping habit and online shopping experience have similar effects on repurchase intention. They both have positive mediated effects through satisfaction and moderate the relationship between satisfaction and online repurchase intention. The results of a survey study involving 122 online customers provide strong support for our research model. We also identify after-sale service, transaction efficiency, security, convenience, and cost savings as important online shopping usefulness drivers. Theoretical and practical implications include establishing a contingency theory to more fully explain online customer retention as well as guidelines for development of customer relationship management initiatives.

Academic writing is an essential component of research, characterized by structured expression of ideas, data-driven arguments, and logical reasoning. However, it poses challenges such as handling vast amounts of information and complex ideas. The integration of Artificial Intelligence (AI) into academic writing has become increasingly important, offering solutions to these challenges. This review aims to explore specific domains where AI significantly supports academic writing. A systematic review of literature from databases like PubMed, Embase, and Google Scholar, published since 2019, was conducted. Studies were included based on relevance to AI's application in academic writing and research, focusing on writing assistance, grammar improvement, structure optimization, and other related aspects. The search identified 24 studies through which six core domains were identified where AI helps academic writing and research: 1) facilitating idea generation and research design, 2) improving content and structuring, 3) supporting literature review and synthesis, 4) enhancing data management and analysis, 5) supporting editing, review, and publishing, and 6) assisting in communication, outreach, and ethical compliance. ChatGPT has shown substantial potential in these areas, though challenges like maintaining academic integrity and balancing AI use with human insight remain. AI significantly revolutionises academic writing and research across various domains. Recommendations include broader integration of AI tools in research workflows, emphasizing ethical and transparent use, providing adequate training for researchers, and maintaining a balance between AI utility and human insight. Ongoing research and development are essential to address emerging challenges and ethical considerations in AI's application in academia.

This review evaluates the role of Artificial Intelligence (AI) in transforming diagnostic imaging in healthcare. AI has the potential to enhance accuracy and efficiency of interpreting medical images like X-rays, MRIs, and CT scans. A comprehensive literature search across databases like PubMed, Embase, and Google Scholar was conducted, focusing on articles published in peer-reviewed journals in English language since 2019. Inclusion criteria targeted studies on AI's application in diagnostic imaging, while exclusion criteria filtered out irrelevant or empirically unsupported studies. Through 30 included studies, the review identifies four AI domains and eight functions in diagnostic imaging: 1) In the area of Image Analysis and Interpretation, AI capabilities enhanced image analysis, spotting minor discrepancies and anomalies, and by reducing human error, maintaining accuracy and mitigating the impact of fatigue or oversight, 2) The Operational Efficiency is enhanced by AI through efficiency and speed, which accelerates the diagnostic process, and cost-effectiveness, reducing healthcare costs by improving efficiency and accuracy, 3) Predictive and Personalised Healthcare benefit from AI through predictive analytics, leveraging historical data for early diagnosis, and personalised medicine, which employs patient-specific data for tailored diagnostic approaches, 4) Lastly, in Clinical Decision Support, AI assists in complex procedures by providing precise imaging support and integrates with other technologies like electronic health records for enriched health insights, showcasing ai's transformative potential in diagnostic imaging. The review also discusses challenges in AI integration, such as ethical concerns, data privacy, and the need for technology investments and training. AI is revolutionising diagnostic imaging by improving accuracy, efficiency, and personalised healthcare delivery. Recommendations include continued investment in AI, establishment of ethical guidelines, training for healthcare professionals, and ensuring patient-centred AI development. The review calls for collaborative efforts to integrate AI in clinical practice effectively and address healthcare disparities.

Purpose Given the proliferation of mobile devices, m‐commerce is expected to experience a substantial growth. However, most m‐commerce applications except for a few have failed to meet expectations. In this study, the authors aim to examine specific factors pertaining to the individual adoption of B2C transactional mobile commerce. Design/methodology/approach A comprehensive framework integrating well established theories of technology adoption – i.e. the technology acceptance model (TAM) and the theory of planned behaviour (TPB) – is developed. More specifically, perceived usefulness is re‐conceptualized to enhance the specificity of these theories to mobile commerce. The resulting model is empirically tested with mobile device users who have not adopted mobile commerce yet. Findings The empirical results provide strong support for the integrative approach, shedding light on the significance and relative importance of specific technological characteristics. The theoretical and empirical implications of these results are discussed. Originality/value The paper demonstrates the need to develop the innovation diffusion theory and TAM further by including the effects of social influence and individual characteristic variables. Furthermore, the paper also shows the usefulness of accounting for the specificity of the IT artifact in general and m‐commerce applications in particular. In this study, the specificity of the IT artifact is accounted for by decomposing perceived usefulness into specific considerations that are relevant to m‐commerce adoption. Such an approach presents a major advantage. Indeed, the significance and magnitude of the formative measures show which characteristics of m‐commerce are adoption drivers.