Explore the vast range of titles available.

Blockchain technologies have received considerable attention from academia and industry due to their distinctive characteristics, such as data integrity, security, decentralization, and reliability. However, their adoption rate is still scarce, which is one of the primary reasons behind conducting studies related to users’ satisfaction and adoption. Determining what impacts the use and adoption of Blockchain technologies can efficiently address their adoption challenges. Hence, this systematic review aimed to review studies published on Blockchain technologies to offer a thorough understanding of what impacts their adoption and discuss the main challenges and opportunities across various sectors. From 902 studies collected, 30 empirical studies met the eligibility criteria and were thoroughly analyzed. The results confirmed that the technology acceptance model (TAM) and technology–organization–environment (TOE) were the most common models for studying Blockchain adoption. Apart from the core variables of these two models, the results indicated that trust, perceived cost, social influence, and facilitating conditions were the significant determinants influencing several Blockchain applications. The results also revealed that supply chain management is the main domain in which Blockchain applications were adopted. Further, the results indicated inadequate exposure to studying the actual use of Blockchain technologies and their continued use. It is also essential to report that existing studies have examined the adoption of Blockchain technologies from the lens of the organizational level, with little attention paid to the individual level. This review is believed to improve our understanding by revealing the full potential of Blockchain adoption and opening the door for further research opportunities.

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.

Cybersecurity procedures and policies are prevalent countermeasures for protecting organizations from cybercrimes and security incidents. Without considering human behaviors, implementing these countermeasures will remain useless. Cybersecurity behavior has gained much attention in recent years. However, a systematic review that provides extensive insights into cybersecurity behavior through different technologies and services and covers various directions in large-scale research remains lacking. Therefore, this study retrieved and analyzed 2210 articles published on cybersecurity behavior. The retrieved articles were then thoroughly examined to meet the inclusion and exclusion criteria, in which 39 studies published between 2012 and 2021 were ultimately picked for further in-depth analysis. The main findings showed that the protection motivation theory (PMT) dominated the list of theories and models examining cybersecurity behavior. Cybersecurity behavior and intention behavior counted for the highest purpose for most studies, with fewer studies focusing on cybersecurity awareness and compliance behavior. Most examined studies were conducted in individualistic contexts with limited exposure to collectivistic societies. A total of 56% of the analyzed studies focused on the organizational level, indicating that the individual level is still in its infancy stage. To address the research gaps in cybersecurity behavior at the individual level, this review proposes a number of research agendas that can be considered in future research. This review is believed to improve our understanding by revealing the full potential of cybersecurity behavior and opening the door for further research opportunities.

Google classroom can work in unidirectional process as it can serve the teachers’ strategies and styles on one hand and students’ perception, understanding, and effective participation in different classroom skills on the other hand. The ac-ceptance of Google classroom is affected by different factors. Some of them are still not clearly specified and discussed in previous research; therefore, they need further investigation. Based on the previous assumption, this study is an attempt to examine the factors that affect the students’ acceptance of Google classroom at Al Buraimi University College (BUC) in Oman. The Technology Acceptance Model (TAM) was adopted to formulate the hypotheses of the current study. The data was collected through an online questionnaire with 337 respondents. The Partial Least Square-Structural Equation Model (PLS-SEM) approach was used to assess both the measurement and structural models. The results of the study prove that both the perceived ease of use (PEOU) and perceived usefulness (PU) positively influence the behavioral intention, which in turn influence the actual usage of Google classrooms. This study helps the decision makers of the higher educational institutions to have a better understanding of the effectiveness of us-ing Google classroom by their students. It is assumed that it helps in measuring the level of students’ acceptance to the previously mentioned technology.

Integration between information systems is critical, especially in the healthcare domain, since interoperability requirements are related to patients’ data confidentiality, safety, and satisfaction. The goal of this study is to propose a solution based on the integration between queue management solution (QMS) and the electronic medical records (EMR), using Health Level Seven (HL7) protocols and Extensible Markup Language (XML). The proposed solution facilitates the patient’s self-check-in within a healthcare organization in UAE. The solution aims to help in minimizing the waiting times within the outpatient department through early identification of patients who hold the Emirates national ID cards, i.e., whether an Emirati or expatriates. The integration components, solution design, and the custom-designed XML and HL7 messages were clarified in this paper. In addition, the study includes a simulation experiment through control and intervention weeks with 517 valid appointments. The experiment goal was to evaluate the patient’s total journey and each related clinical stage by comparing the “routine-based identification” with the “patient’s self-check-in” processes in case of booked appointments. As a key finding, the proposed solution is efficient and could reduce the “patient’s journey time” by more than 14 minutes and “time to identify” patients by 10 minutes. There was also a significant drop in the waiting time to triage and the time to finish the triage process. In conclusion, the proposed solution is considered innovative and can provide a positive added value for the patient’s whole journey.

The association between information management and cloud computing has been supported substantially in the existing literature. However, little is known about the role of national culture in this relationship. Accordingly, this study aims to investigate the role of national culture in the relationship between information management and the adoption of cloud computing. A total of 300 Turkish and 349 Malay undergraduate students were recruited for this study. A multi-group structural equation modeling approach is used to investigate cross-cultural differences. The results showed that the relationship between perceived ease of use and usefulness was positively significant for both countries, but this relationship was stronger for Malaysia. Similarly, the relationship between perceived ease of use and behavioral intention was stronger for Malaysia. The results also indicated that perceived usefulness significantly predicts behavioral intention for Turkey, while this relationship was not significant for Malaysia. The results showed that information management impacts perceived usefulness in Turkey, while this relationship was not supported in Malaysia. In conclusion, the findings revealed that national culture plays an essential role in the relationship between information management and the adoption of cloud computing.

People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic. The high-frequency words such as “death”, “test”, “spread”, and “lockdown” suggest that people fear of being infected, and those who got infection are afraid of death. The results also showed that people agreed to stay at home due to the fear of the spread, and they were calling for social distancing since they become aware of the COVID-19. It can be suggested that social media posts may affect human psychology and behavior. These results may help governments and health organizations to better understand the psychology of the public, and thereby, better communicate with them to prevent and manage the panic.

Studying the factors that affect the e-learning adoption is not a new research topic. Nevertheless, exploring the effect of knowledge acquisition and knowledge sharing on e-learning adoption is a relatively new research trend that has not been featured in the existing literature. Thus, this study was conducted to build a new model by extending the technology acceptance model (TAM) with knowledge acquisition and knowledge sharing to examine the e-learning adoption. A total of 403 students enrolled at Al Buraimi University College (BUC) in Oman was surveyed. Using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate the proposed model, the results suggested that knowledge acquisition, knowledge sharing, perceived usefulness, and perceived ease of use have significant direct effects on the students’ behavioral intention to adopt e-learning systems. The findings also suggested that knowledge acquisition and knowledge sharing have a significant positive influence on perceived usefulness and perceived ease of use. The evidence from these results provides holistic insights which could assist the policy-makers and educators to better understand the factors affecting the adoption of e-learning systems. The implications for theory and practice, limitations, and future work are also discussed.

Artificial intelligence (AI)-based voice assistants have become an essential part of our daily lives. Yet, little is known concerning what motivates students to use them in educational activities. Therefore, this research develops a theoretical model by extending the technology acceptance model (TAM) with subjective norm, enjoyment, facilitating conditions, trust, and security to examine students’ use of AI-based voice assistants for instructional purposes. The developed model was then validated based on data collected from 300 university students using the PLS-SEM technique. The results supported the role of enjoyment, trust, and perceived ease of use (PEOU) in affecting the perceived usefulness (PU) of voice assistants. The empirical results also showed that facilitating conditions and trust in technology strongly influence the PEOU. Contrary to the extant literature, the results indicated that subjective norm, facilitating conditions, and security did not impact PU. Similarly, subjective norm and enjoyment did not affect PEOU. This research is believed to add a holistic understanding of the key drivers affecting students’ use of voice assistants for educational purposes. It offers several theoretical contributions and practical implications on how to successfully employ these assistants.

To the best of our knowledge, much research has been conducted concerning the topic of mobile learning (m-learning) adoption or acceptance. However, examining the continued use of m-learning is still in short supply and calling for further research. To bridge this limitation, this study develops an integrated model through the integration of three different theoretical models, namely technology acceptance model (TAM), theory of planned behavior (TPB), and expectation-confirmation model (ECM). To examine the proposed model, a questionnaire survey was developed to collect data from 273 postgraduate students enrolled at The British University in Dubai in the United Arab of Emirates (UAE). The partial least squares-structural equation modeling (PLS-SEM) is used to analyze the collected data. The empirical results indicated that perceived ease of use, attitude, perceived behavioral control, and subjective norms are significant predictors to explain the continued use of m-learning. Nevertheless, perceived usefulness and satisfaction were shown to be insignificant determinants to continuous intention. Further theoretical and practical implications are also discussed.