Despite the evident growth of the Internet of Things (IoT) applications, IoT deployments in organizations remain in their early stages. This paper aims to systematically review and analyze the existing literature on IoT adoption in organizations. The extant literature was identified using five electronic databases from 2015 to July 2021. Seventy-seven articles have met the eligibility criteria and were analyzed to answer the research questions. This study produced a coherent taxonomy that can serve as a framework for future research on IoT adoption in organizations. This paper presents an overview of the essential features of this emerging technology in terms of IoT adoption benefits and challenges in organizations. Existing theoretical models have been analyzed to identify the factors that influence IoT adoption and to understand the future requirements for widespread IoT adoption in organizations. Six critical factors affecting and playing a key role in IoT adoption in organizations were identified based on the critical review findings: technological, organizational, environmental, human, benefit, and value. Decision-makers and developers can prioritize these critical factors and progressively improve their development to enhance IoT adoption efficiency. This review also includes an in-depth analysis to bridge gaps and provide a comprehensive overview to further understand this research field.

Online learning has replaced traditional face-to-face classroom instruction in the educational system. Learning via mobile, or mobile learning, is one of the solutions that most learners use since it is less expensive and easier to adopt on the go. However, in addition to hindering information transfer, issues such as small screen size and bad interface design can also make learning more cognitively demanding. This paper presents a systematic literature review on the user interface design of mobile learning applications based on the preferred reporting items for systematic reviews and meta-analyses approach. Articles selected for this study were published after the COVID-19 outbreak, between 2020 and 2022. The goal of this research was to outline the current user interface design criteria and guidelines applied when designing a mobile learning application and explore how these factors affect the learner’s cognitive load. It also aimed to identify potential research gaps and future opportunities in the creation of a UID guideline/framework for mobile learning. The findings of this study may be used as a guideline for designers, developers, educators, instructors, and others who are interested in creating a mobile learning application that provides learners with an effective knowledge and mobile learning experience.

The manufacturing sector (MS) is considered one of the most important national economic sectors; therefore, global manufacturers strive to apply cutting-edge technologies to gain competitive advantages. The Internet of Things (IoT) has an inherent potential to enhance MS economic growth and maintain its dominance in global markets by using a vast network of smart sensors; nevertheless, IoT technology adoption in the MS remains in the early phase. This research aims to define the antecedents that affect IoT adoption in the MS and propose a conceptual model to explain the adoption intention. Based on an extensive literature review, the proposed model was constructed by three main antecedents: perceived value, perceived benefits, and perceived challenges, and 11 related variables. The model development used a multi-theoretical perspective by integrating three theories: the value-based adoption model, the diffusion of innovation theory, and the technology–organization–environment framework. This study provides decision-makers with valuable insight that promotes IoT adoption in MS and enriches the literature with a new perspective that encourages more studies on IoT adoption in organizations.

Cloud computing adoption in e-government offers significant benefits yet presents unique challenges, particularly for developing countries. While existing research has focused on technical aspects, this study expands the analysis to investigate broader determinants of cloud adoption from technological, organizational, environmental, and individual perspectives. A systematic literature review was conducted across six scholarly databases and identified 40 primary studies. The findings emphasize the importance of adopting an integrated organizational-individual perspective to understand cloud adoption better. The study proposes a multi-dimensional conceptual framework for cloud adoption in government organizations and recommends further empirical validation of this model. This review provides decision-makers and managers in the government sector with practical insights for addressing adoption challenges, enhancing service delivery, and increasing cloud computing acceptance rate within government organizations.

Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.