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The purpose of this paper is to assess nurse’s knowledge and practice regarding basic life support (BLS) skills while working with SARS-CoV-2 patients in Jordanian hospitals.

A cross-sectional survey was conducted among 386 nurses with direct contact with SARS-CoV-2 patients at Jordanian hospitals. A self-administered structured questionnaire was used based on the American Heart Association (AHA) guidelines.

A total of 386 participants were recruited. The mean years of experience were 7.89 (SD = 5.97). About three quarters of participants revealed they deal with SARS-CoV-2 patients directly (n = 284, 73.6%). The total mean score of nurse’s knowledge was 4.44 (SD = 1.22), while the total mean score of practice was 8.44 (SD = 2.05). Independent t-test was used, which revealed a statistically significant difference between educational level and total score of nurse’s knowledge [t(386) = 0.215 and p = 0.001] and between training to deal with SARS-CoV-2 during BLS and total score of practice [t(386) = 2.66 and p = 0.008]. Pearson correlation discloses a positive correlation between the total score of knowledge and practice (r = 0.343 and p = 0.001).

In general, nurses revealed a moderate level of knowledge and practice of BLS skills. However, assessing nurse’s knowledge and practice during the outbreak of SARS-CoV-2 plays a key role in identifying the gap in nurse’s knowledge and practice, and therefore, it will have an impact on providing high-quality BLS to save infected patients while providing maximum safety according to AHA guidelines.

This study is the first study that examined the level of knowledge and practice of BLS skills during SARS-CoV-2 pandemic in Jordan.

This chapter offers a systematic review of research into quality assurance and quality management in higher education. It begins by considering quality as theory and discusses the methodology applied. The origin and meaning of the terms quality assurance and quality management, as they are used in higher education, and their application and practice, are then discussed. The issues and critiques that have been raised concerning quality assurance and management are identified, before some conclusions are reached.

This study aims to focus on exploring the impact of the COVID-19 pandemic on the students’ academic performance in Jordanian higher education during the outbreak of the pandemic, evaluating the e-learning education and evaluating online education in practical lessons during the pandemic according to the university type, educational level, academic year and different specializations or faculty.

This study provides quantitative and qualitative analysis on the students’ e-learning performance during the pandemic. It presents the analysis of online learning preference of 424 questionnaires and 85 structured interviews with the university’s students and examines whether there is significant impact of the COVID-19 pandemic on their academic performance.

The findings of this study present evidence of students’ improvement in their academic performance and lend credence to the notion that organizational characteristics may play a role in the adaptation of emergency remote teaching. This was evident that more scientific-based faculties (engineering, pharmaceutical and medicine) faced more challenges during the pandemic, and it negatively influenced students’ performance. This was justifiable to the need for a more practical one-to-one interaction and integration.

The findings of this research present evidence of students’ improvement in their academic performance and lend credence to the notion that organizational characteristics may play a role in the adaptation of emergency remote teaching. This was evident that more scientific-based faculties (engineering, pharmaceutical and medicine) faced more challenges during the pandemic and it negatively influenced students’ performance. This was justifiable to the need for a more practical one-to-one interaction and integration.

The purpose of this study is to empirically test the relationship between intellectual capital (i.e. human capital, structural capital, relational capital) and business performance within the pharmaceutical sector of Jordan.

A valid research instrument was utilized to conduct a survey of 132 top‐ and middle‐level managers from all 15 members of the Jordanian Association of Pharmaceutical Manufacturers.

A correlation and path analysis were conducted to ascertain the validity of the measures and models. Statistical support was found for the hypothesized relationships.

The findings offer valuable insights on the generalizability of intellectual capital in a novel research setting.

Intellectual capital measurement is of primary interest for senior executives of pharmaceutical firms in Jordan.

The research reported is among only a few to investigate the issue of intellectual capital in Egypt and the first to study pharmaceutical firms.

This paper aims to explore the relationship between knowledge creation and intellectual capital (IC) through an empirical study in the pharmaceutical industry. In the current economy, knowledge and IC are considered as the most important organizational assets and are the key resources in gaining competitive advantage.

This paper adopts the socialization, externalization, combination and internalization (SECI) model to examine the format of knowledge creation processes (KCP) and uses a model to demonstrate the relationship between KCP and IC and its components in the pharmaceutical industry. A valid instrument was adopted to collect the required data on KCP and and IC dimensions. Structural equation modeling was used to assess the measurement model and to test the research hypotheses using the data collected from 470 completed questionnaires.

The results supported the research model and revealed that KCP has significant influence on the accumulation of human capital. The performance of human capital manifests significant impact on structural capital and relational capital.

Given the strong association between KCP and IC, managers should define their own robust operations for knowledge creation to improve their IC accumulation.

This research departs from the earlier research on KCP–IC by adopting the SECI model and a research model that facilitates the exploration of the relationship between KCP and IC dimensions in the pharmaceutical industry. The research results provided strong support for the KCP–IC relationship.

This paper aims to examine why the alliance formed between non-Islamist forces and state actors to oust Mohamed Morsi from power in 2013 broke down quickly.

This paper makes use of original data set derived from three waves of surveys fielded in 2011, 2014 and 2015 that ask questions about public threat perception. Around 10 elite interviews were also conducted to further test the study’s hypothesis.

On the one hand, non-Islamists, civic forces challenged the status and interests of state actors in a way that made state actors view them with heightened distrust. On the other, many civic forces, in face of high threat perception, prioritized law during and order after Morsi’s removal, driven – at least partly – by shifts in public attitudes.

Many views look at transitions in the Arab world from the angle of how Islamists interact with traditional power holders. Such an approach, however, could be reductionist in many ways because it disregards civic forces. This is a point this paper seeks to address.

Amidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.

Drawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.

The empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.

The research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.

The cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

Contact tracing apps have emerged to collect data and mitigate the spread of infectious diseases. However, privacy and security concerns have caused individuals to hesitate to adopt these solutions. Our objective is to evaluate the role of the political environment, information privacy, security and users’ intentions to use contact tracing apps.

We scraped the digital app store and collected 399 relevant reviews and other data from 21 contact tracing apps in the USA. A semi-supervised machine learning model was developed to extract information on privacy and security aspects from the reviews.

Our findings show a positive connection between security controls and user adoption, as reflected in star ratings. Users residing in states with blue political environments tend to assign higher ratings to apps, especially when robust security controls are in place. These findings confirm the influence of the political environment on the adoption of contact tracing apps. In times of a pandemic, our findings suggest that users prioritize security over privacy concerns, emphasizing the critical role of strong security features in promoting app acceptance.

This paper emphasizes the political environment of the state offering the app intersects with concerns about security and privacy as well as the effectiveness of security and privacy measures, influencing the app’s ratings. Also, it shows the importance of understanding and addressing the role of the political environment when designing and promoting such public health tools, regardless of the specific disease or outbreak.