A CONCEPTUAL LIFE EVENT FRAMEWORK FOR GOVERNMENT-TO-CITIZEN ELECTRONIC SERVICES PROVISION

In recent years, life event approach has been widely used by governments all over the world for designing and providing web services to citizens through their e-government portals. Despite the wide usage of this approach, there is still a challenge of how to use this approach to design e-government portals in order to automatically provide personalised services to citizens. We propose a conceptual framework for e-government service provision based on life event approach and the use of citizen profile to capture the citizen needs, since the process of finding Web services from a government-to-citizen (G2C) system involves understanding the citizens’ needs and demands, selecting the relevant services, and delivering services that matches the requirements. The proposed framework that incorporates the citizen profile is based on three components that complement each other, namely, anticipatory life events, non-anticipatory life events and recurring services

Exploring business and IT alignment mechanisms: toward a learning perspective

Numerous studies have attempted to develop strategic alignment mechanisms. The strategic alignment mechanism is broken down into two categories namely: strategy process and strategy content. Our review shows that alignment research has been carried out in isolation. We see this as having had the effect of limiting the extent to which executives can understand elements of performance. We confer with a number of researchers in postulating that using a mechanism such as multilevel learning to combine strategy content and strategy process under one metaphor can greatly facilitate, through exploration and exploitation, the understanding not only of human interactions within a firm, but also of the interaction existent between a firm and its environment. The findings in this study further support the idea of integrating strategy process and content to have a better understating of alignment maturity and impact on business performance. It also elaborates the affect of misalignment in companies on performance

The Impact of COVID-19 Pandemic on Student’s E-Learning Experience in Jordan

Since the beginning of the COVID-19 pandemic Universities around the world are taking rapid actions to ensure students learning continuity and secure the well-being of their students. This study aims at exploring the student’s e-learning experience in Jordanian Universities as well as e-learning readiness during the pandemic. While each university is unique, we hope our assessment can provide some insights into how well the student’s e-learning experience was during the pandemic. A structural online questionnaire was distributed, followed by descriptive analysis. Students from remote and disadvantaged areas primarily faced enormous challenges such as technological accessibility, poor internet connectivity, and harsh study environments. This study also highlights the role of electronic commerce in transforming distance learning. Further investments and contingency plans are needed to develop a resilient education system that supports electronic and distance learning throughout Jordan

Towards a life-event oriented G2C e-service provision: the NoBLE Framework

One of the primary features of modern government-to-citizen (G2C) service provision is the ability to offer a citizen-centric view of the e-government portal. Life-event approach is one of the most widely adopted paradigms supporting the idea of solving a complex event in a citizen’s life through a single service provision. Several studies have used this approach to design e-government portals. However, they were limited in terms of use and scalability. There were no mechanisms that show how to specify a life-event for structuring public e-services, or how to systematically match life-events with these services taking into consideration the citizen needs. We introduce the NOrm-Based Life-Event (NoBLE) framework for G2C e-service provision with a set of mechanisms as a guide for designing active life-event oriented e-government portals

The Role of TQMk in Increasing the Effectiveness of E-Marketing within the Jordanian Telecommunication Sector

The current study focused on examining the role of TQMk (Total Quality Marketing) in increasing the effectiveness of e-marketing within Jordanian telecommunication sector; TQMk included variables of service quality, market orientation and the customer-focused approach. A quantitative approach was adopted through utilizing a questionnaire, which was distributed to 18 marketing and project managers within Jordanian telecommunication organizations (Zain, Umniah and Orange). Results of the study indicated that TQMk can have an influence in increasing the effectiveness and efficiency of e-marketing solutions within the organization and mainly within the social marketing and electronic marketing departments, through developing the variable of the customer-focused approach, which has the deepest influence on e-marketing approach’s effectiveness; it was followed by an influence of service quality, and the least influential factor was market orientation. The study recommended focusing on clients within the targeted markets through different aspects, including price, new products acceptance, customer behavior and purchase decision motivators

A New Stock Price Forecasting Method Using Active Deep Learning Approach

Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance

The Effects of Online Learning on Students’ Performance: A Comparison Between UK and Jordanian Universities

The global pandemic of Covid-19 has caused lockdowns across the globe, causing education institutions to shut down. As a result, classes have been held online. This study investigates the impact of online learning on student performance by comparing the impact on Jordan and the UK. Both countries have been reported to have high technological competency but are known to have varying sociodemographic structures. Surveys were conducted on undergraduate students from both countries (N = 780) to analyse students’ perception of online learning, self-perception of academic capabilities, and faculty performance during online learning. Semi-structured interviews were conducted on professors from both countries (N = 8). The findings indicate that both Jordan and the UK have been very similarly affected by in terms of student performance, with major challenges being in communication, technological competency, access to hardware for taking online classes, absenteeism, and drop-outs. Some benefits to student performance were identified as having access to recorded lectures, having more access to faculties through e-mail and extended office hours. Ethical implications were not commented on. Privacy concerns were largely voiced by faculties

Upshots of Intrinsic Traits on Social Entrepreneurship Intentions among Young Business Graduates: An Investigation through Moderated-Mediation Model

Social entrepreneurship has recently become a much-desired area of research for academia, practices, and policymaking. Natural or cognitive personal thoughtfulness like loving-kindness meditation (LKM) and compassion trigger individual intentions towards the social entrepreneurial venture. In this process of individual social entrepreneurial intention personality trait plays a very vital role, such as entrepreneurship resilience. For this study, a purposive sampling technique was incorporated and data was collected from 631 business and management sciences students. Data is analyzed by SPSS 23 and for the hypothesis testing, we used the bootstrap analysis of Hayes PROCESS v3.5. This study depicts that LKM has a positive significant impact on compassion and no significant impact on social entrepreneurship intentions while resilience strengthens the direct relationship of compassion with social entrepreneurship and the indirect relationship of LKM with social entrepreneurship via compassion. This study contributes to solving the economic and social problems over the globe especially by boosting the LKM and resilience traits so that the young graduate commence social entrepreneurship. This study helps the academician and policymakers to adopt strategies through which they can encourage youth to indulge in social entrepreneurial ventures solve the social problem and decrease unemployment

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis