Studies have shown that trust plays a crucial role in the consumers’ decision to adopt Internet of Things (IoT) technologies and services since it helps them to overcome perceptions of risk and uncertainty related to it and enhances the customers’ level of acceptance and adoption intention. Nevertheless, the literature of IoT still lacks studies on the behavioral aspect that explain the customers’ perception towards IoT adoption and focuses more on technological aspect. The main goal of this study is to examine the factors that influence consumer trust and their role in the adoption of IoT technology. A conceptual trust model that encompasses the major factors affecting trust towards IoT technology adoption has been presented. The model is composed of three dimensions of factors that we assume will influence the level of trust which are: product related factors, social influence related factors and security related factors. This framework is validated through surveying consumers’ opinions, which provide views and feedback regarding factors influencing their trust towards this technology. The model can assist researchers to further investigate the trust issues and create a trustworthy literature to guide IoT products’ development and marketing strategies that are focused on the consumer’s requirements.

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures.

Blockchain is an emerging technology that is increasingly being applied in both industrial and academic contexts. Cryptocurrency is a major application of blockchain in the financial sector, but the technology is expected to disrupt other industries. In fact, it has influenced many businesses and reshaped private and public sector activities. Therefore, there is growing interest in blockchain-based solutions, and applications have evolved in finance, insurance, logistics, government, education, and healthcare. Applications built on blockchains benefit from fair access, transparency, and immutability; these properties have attracted business owners and practitioners to explore blockchain opportunities beyond cryptocurrency, motivating them to investigate how they can benefit from the technology and also to evaluate its compatibility with their strategic orientation. Suitability evaluation (or applicability evaluation) has always been a crucial step for the successful adaptation of any innovative technology, including blockchain. Hence, this paper investigates how the current literature has addressed blockchain suitability evaluation for business cases beyond cryptocurrency. A scoping review is presented that examines the evaluation models and frameworks that have been developed to assist decision-makers regarding blockchain adoption. The results indicate that blockchain evaluation methodologies have utilized varied approaches and serve diverse objectives, which are applicable for different technology adoption stages. Through this scoping review, blockchain evaluation initiatives are classified into five categories, and a critical analysis is offered of the evaluation models under each category. As such, this scoping review overviews existing methodologies for blockchain evaluation approaches with a focus on context, identified assessments factors, assessment process, and evaluation dimensions.

Saudi universities have suspended the traditional face-to-face classes and moved to online learning to ensure the continuation of the educational process during the COVID-19 pandemic. The speed at which the move to online learning has happened was unprecedented and staggering. Academics, had to improvise and try different e-learning solutions in less-than-ideal circumstances. No matter how powerful and easy-to-use e-learning solutions might be, many academics understandably found the transition to e-learning stressful. This research project looks at the transition experience to online learning from the viewpoint of academics in Saudi universities. It investigates how the COVID-19 pandemic has affected the adoption of e-learning solutions and how this forced experience would affect the long-term adoption of e-learning solutions. The study employed an online survey administered to academics from all the 24 public universities. The results were quantitatively analysed by using structural equation modelling (SEM). It was found that attitudes toward e-learning, self-efficacy and perceived reliability have significant positive effects on behavioural intention to adopt e-learning. It was also found that COVID-19 has positively affected the long-term adoption of e-learning. Therefore, it is recommended that universities take immediate actions and capitalise on the e-learning experience that academics have gained during COVID-19 pandemic.