Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Aim/Purpose: This study examined the use of Social Media Applications (SMAs) for accessing health-related information within a heterogeneous population in Jordan. The objective of this study was therefore threefold: (i) to investigate the usage of SMAs, including WhatsApp, Twitter, YouTube, Snapchat, Instagram, and Facebook, for accessing health-related information; (ii) to examine potential variations in the use of SMAs based on demographic and behavioral characteristics; and (iii) to identify the factors that can predict the use of SMAs. Background: There has been limited focus on investigating the behavior of laypeople in Jordan when it comes to seeking health information from SMAs. Methodology: A cross-sectional study was conducted among the general population in Jordan using an online questionnaire administered to 207 users. A purposive sampling technique was employed, wherein all the participants actively sought online health information. Descriptive statistics, t-tests, and regression analyses were utilized to analyze the collected data. Contribution: This study adds to the existing body of research on health information seeking from SMAs in developing countries, with a specific focus on Jordan. Moreover, laypeople, often disregarded by researchers and health information providers, are the most vulnerable individuals who warrant greater attention. Findings: The findings indicated that individuals often utilized YouTube as a platform to acquire health-related information, whereas their usage of Facebook for this purpose was less frequent. Participants rarely utilized Instagram and WhatsApp to obtain health information, while Twitter and Snapchat were very seldom used for this purpose. The variable of sex demonstrated a notable positive correlation with the utilization of YouTube and Twitter for the purpose of finding health-related information. Conversely, the variable of nationality exhibited a substantial positive correlation with the utilization of Facebook, Instagram, and Twitter. Consulting medical professionals regarding information obtained from the Internet was a strong indicator of using Instagram to search for health-related information. Recommendations for Practitioners: Based on the empirical results, this study provides feasible recommendations for the government, healthcare providers, and developers of SMAs. Recommendation for Researchers: Researchers should conduct separate investigations for each application specifically pertaining to the acquisition of health-related information. Additionally, it is advisable to investigate additional variables that may serve as predictors for the utilization of SMAs. Impact on Society: The objective of this study is to enhance the inclination of the general public in Jordan to utilize SMAs for health-related information while also maximizing the societal benefits of these applications. Future Research: Additional research is required to examine social media’s usability (regarding ease of use) and utility (comparing advantages to risks) in facilitating effective positive change and impact in healthcare.

Aim/Purpose: This study investigates the drivers of user satisfaction in e-government services and its influence on continued use intention and citizen trust in government. It employs the integration of the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM). Background: Electronic government, transforming citizen-state interactions, has gained momentum worldwide, including in India, where the aim is to leverage technology to improve citizen services, streamline administration, and engage the public. While prior research has explored factors influencing citizen satisfaction with e-government services globally, this area of study has been relatively unexplored in India, particularly in the post-COVID era. Challenges to widespread e-government adoption in India include a large and diverse population, limited digital infrastructure in rural areas, low digital literacy, and weak data protection regulations. Additionally, global declines in citizen trust, attributed to economic concerns, corruption, and information disclosures, further complicate the scenario. This study seeks to investigate the influence of various factors on user satisfaction and continuance usage of e-government services in India. It also aims to understand how these services contribute to building citizens’ trust in government. Methodology: The data were collected by utilizing survey items on drivers of e-government services, user satisfaction, citizen trust, and continuance use intention derived from existing literature on information systems and e-government. Responses from 501 Indian participants, collected using an online questionnaire, were analyzed using PLS-SEM. Contribution: This study makes a dual contribution to the e-government domain. First, it introduces a comprehensive research model that examines factors influencing users’ satisfaction and continuance intention with e-government services. The proposed model integrates the TAM and ISSM. Combining these models allows for a comprehensive examination of e-government satisfaction and continued intention. By analyzing the impact of user satisfaction on continuance intention and citizen trust through an integrated model, researchers and practitioners gain insights into the complex dynamics involved. Second, the study uncovers the effects of residential status on user satisfaction, trust, and continuance intention regarding e-government services. Findings reveal disparities in the influence of system and service quality on user satisfaction across different user segments. Researchers and policymakers should consider these insights when designing e-government services to ensure user satisfaction, continuance intention, and the building of citizen trust. Findings: The findings indicate that the quality of information, service, system, and perceived usefulness play important roles in user satisfaction with e-government services. All hypothesized paths were significant, except for perceived ease of use. Furthermore, the study highlights that user satisfaction significantly impacts citizen trust and continuance use intention. Recommendations for Practitioners: The findings suggest that government authorities should focus on delivering accurate, comprehensive, and timely information in a secure, glitch-free, and user-friendly digital environment. Implementing an interactive and accessible interface, ensuring compatibility across devices, and implementing swift query resolution mechanisms collectively contribute to improving users’ satisfaction. Conducting awareness and training initiatives, providing 24×7 access to online tutorials, helpdesks, technical support, clear FAQs, and integrating AI-driven customer service support can further ensure a seamless user experience. Government institutions should leverage social influence, community engagement, and social media campaigns to enhance user trust. Promotional campaigns, incentive programs, endorsements, and user testimonials should be used to improve users’ satisfaction and continuance intention. Recommendation for Researchers: An integrated model combining TAM and ISSM offers a robust approach for thoroughly analyzing the diverse factors influencing user satisfaction and continuance intention in the evolving digitalization landscape of e-government services. This expansion, aligning with ISSM’s perspective, enhances the literature by demonstrating how user satisfaction impacts continuance usage intention and citizen trust in e-government services in India and other emerging economies. Impact on Society: Examining the factors influencing user satisfaction and continuance intention in e-government services and their subsequent impact on citizen trust carries significant societal implications. The findings can contribute to the establishment of transparent and accountable governance practices, fostering a stronger connection between governments and their citizens. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope by incorporating a larger sample size could enable a more thorough analysis. Alternatively, delving into the performance of specific e-government services would offer greater precision, considering that this study treats e-government services generically. Additionally, incorporating in-depth interviews and longitudinal studies would yield a more comprehensive understanding of the dynamic evolution of digitalization.

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

Alzheimer's disease (AD), a leading cause of dementia and mortality, presents a growing concern due to its irreversible progression and the rising costs of care. Early detection is crucial for managing AD, which begins with memory deterioration caused by the damage to neurons involved in cognitive functions. Although incurable, treatments can manage its symptoms. This study introduces a hybrid AlexNet+ResNet-50 model for AD diagnosis, utilising a pre-trained convolutional neural network (CNN) through transfer learning to analyse MRI scans. This method classifies MRI images into Alzheimer's disease (AD), moderate cognitive impairment (MCI), and normal control (NC), enhancing model efficiency without starting from scratch. Incorporating transfer learning allows for refining the CNN to categorise these conditions accurately. Our previous work also explored atlas-based segmentation combined with a U-Net model for segmentation, further supporting our findings. The hybrid model demonstrates superior performance, achieving 94.21% accuracy in identifying AD cases, indicating its potential as a highly effective tool for early AD diagnosis and contributing to efforts in managing the disease's impact.

Scientific experiments, simulations, and modern applications generate large amounts of data. Analysing resources required to process such big datasets is essential to identify application running costs for cloud or in-house deployments. Researchers have proposed keeping data in raw formats to avoid upfront utilisation of resources. However, it poses reparsing issues for frequently accessed data. The paper discusses detailed comparative analysis of resources required by in-situ engines and traditional database management systems to process a real-world scientific dataset. A resource monitoring framework has been developed and incorporated into the raw data query processing framework to achieve this goal. The work identified different query types best suited to a given data processing tool in terms of data to result time and resource requirements. The analysis of resource utilisation patterns has led to the development of query complexity aware (QCA) and resource utilisation aware (RUA) data partitioning techniques to process big raw data efficiently. Resource utilisation data have been analysed to estimate the data processing capacity of a given machine.

Large graphs or automata have their data that cannot fit in a single machine, or may take unreasonable time to be processed. We implement with MapReduce and Giraph two algorithms for intersecting and minimising large and distributed automata. We provide some comparative analysis, and the experiment results are depicted in figures. Our work experimentally validates our propositions as long as it shows that our choice, in comparison with MapReduce one, is not only more suitable for graph-oriented algorithms, but also speeds the executions up. This work is one of the first steps of a long-term goal that consists in a high level distributed graph processing language.

The purpose of this research is the exploration of the opinions and level of self-efficacy in the usage of Information Communication Technologies (ICTs) of teachers in Greek pre-schools in the learning process and administration of nurseries. By using the term “usage and utilisation of ICTs in the learning process” we mean the utilisation of the capabilities that new technologies offer in an educationally appropriate way so that the learning process yields positive results. By using the term “self-efficacy” we describe the strength of one’s belief in one’s own ability to use the capabilities he or she possess. In this way, the beliefs of the person in his or her ability to use a personal computer constitute the self-efficacy in computer usage. The research sample consists of 128 pre-school teachers that work in the prefecture of Kavala. Kavala’s prefecture is a representative example of an Education Authority since it consists of urban, suburban, and rural areas. The approach that is deemed to be the most appropriate for the exploration of such research questions is content analysis methodology and correlation analysis. The main findings of the study have shown statistically significant differences regarding the opinions and stances of the pre-school teachers for the introduction of the ICTs in the administration and the usage and utilisation of ICTs in the administration and preparation of teaching. Lastly, there were statistically significant differences between the opinions and stances of the pre-school teachers for the usage and utilisation of ICTs in the learning process. Instead, there were no statistically significant differences regarding the level of self-efficacy of the pre-school teachers in the usage and utilisation of the ICTs in the learning process. The research results could be used in the educational field as well as by Greek Ministry of Education, Research and Religious Affairs in order to take any corrective action, after the effort of Greek Ministry of Education, Research and Religious Affairs, to integrate ICT in the learning process with training courses since 2006.

Aim/Purpose: Significant urban digital divide exists in Nairobi County where low income households lack digital literacy skills and do not have access to the internet. The study was undertaken as an intervention, designed to close the digital divide among low income households in Nairobi by introducing internet access using the domestication framework. Background: Information and Communication Technologies (ICTs) have the potential to help reduce social inequality and have been hailed as critical to the achievement of the Sustainable Development goals (SDGs). Skills in use of ICTs have also become a prerequisite for almost all forms of employment and in accessing government services, hence, the need for digital inclusion for all. Methodology: In this research study, I employed a mixed methods approach to investigate the problem. This was achieved through a preliminary survey to collect data on the existence of urban digital divide in Nairobi and a contextual analysis of the internet domestication process among the eighteen selected case studies. Contribution: While there have been many studies on digital divide between Africa and the rest of the world, within the African continent, among genders and between rural and urban areas at national levels, there are few studies exploring urban digital divide and especially among the marginalized communities living in the low-income urban areas. Findings: Successful domestication of internet and related technologies was achieved among the selected households, and the households appreciated the benefits of having and using the internet for the first time. A number of factors that impede use of internet among the marginalized communities in Nairobi were also identified. Recommendations for Practitioners: In the study, I found that use of differentiated costs internet services targeting specific demographic groups is possible and that use of such a service could help the marginalized urban communities’ access the internet. Therefore, ISPs should offer special internet access packages for the low-income households. Recommendation for Researchers: In this research study, I found that the urban digital divide in Nairobi is an indication of social economic development problems. Therefore, researchers should carryout studies involving multipronged strategies to address the growing digital divide among the marginalized urban communities. Impact on Society: The absence of an Information and Communication Technology (ICT) inclusion policy is a huge setback to the achievement of the SDGs in Kenya. Digital inclusion policies prioritizing digital literacy training, universal internet access and to elucidate the social-economic benefits of internet access for all Kenyans should be developed. Future Research: Future studies should explore ways of providing affordable mass internet access solutions among the residents of low-income communities and in eliminating the persistence urban digital divide in Kenya.

Aim/Purpose: To develop a framework for utilizing Mobile Assisted Language Learning (MALL) to assist non-native English migrant women to acquire English vocabulary in a non-formal learning setting. Background: The women in this study migrated to Australia with varied backgrounds including voluntary or forced migration, very low to high levels of their first language (L1), low proficiency in English, and isolated fulltime stay-at-home mothers. Methodology: A case study method using semi-structured interviews and observations was used. Six migrant women learners attended a minimum of five non-MALL sessions and three participants continued on and attended a minimum of five MALL sessions. Participants were interviewed pre- and post-sessions. Data were analysed thematically. Contribution: The MALL framework is capable of enriching migrant women’s learning experience and vocabulary acquisition. Findings: Vocabulary acquisition occurred in women from both non-MALL and MALL environment; however, the MALL environment provided significantly enriched vocabulary learning experience. Future Research: A standardised approach to measure the effectiveness of MALL for vocabulary acquisition among migrant women in non-formal setting

Aim/Purpose: The digital divide and educational inequalities remain a significant societal problem in the United States, and elsewhere, impacting low income, first-generation, and minority learners. Accordingly, institutions of higher education are challenged to meet the needs of students with varying levels of technological readiness with deficiencies in information and digital literacy shown to be a hindrance to student success. This paper documents the efforts of a mid-Atlantic minority-serving institution as it seeks to assess, and address, the digital and information literacy skills of underserved students Background: A number of years ago, a historically Black university in Maryland developed an institutional commitment to the digital and information literacy of their students. These efforts have included adoption of an international certification exam used as a placement test for incoming freshmen; creation of a Center for Student Technology Certification and Training; course redesign, pre and post testing in computer applications courses; and a student perception survey. Methodology: A multi-methodological approach was applied in this study which relied on survey results, pre and post testing of students enrolled in introductory and intermediate computer applications courses, and scores from five years of placement testing. Student pre and post test scores were compared in order to examine degree of change, and post test scores were also assessed against five years of scores from the same test used as a placement for incoming freshmen. Finally, a student perception and satisfaction survey was administered to all students enrolled in the courses under consideration. The survey included a combination of dichotomous, Likert-scaled, and ranking questions and was administered electronically. The data was subsequently exported to Microsoft Excel and SPSS where descriptive statistical analyses were conducted. Contribution: This study provides research on a population (first-generation minority college students) that is expanding in numbers in higher education and that the literature reports as being under-prepared for academic success. Unfortunately, there is a paucity of current studies examining the information and technological readiness of students specifically enrolled at minority serving institutions. As such, this paper is timely and relevant and helps to extend our discourse on the digital divide and technological readiness as it impacts higher education. The students included in this study are representative of those enrolled in Historically Black Colleges or Universities (HBCUs) in the United States, giving this paper broad implications across the country. Internationally, most countries have populations of first-generation college students from under-served populations for whom a lack of digital readiness is an also an issue therefore giving this study a global relevance. Findings: The digital divide is a serious concern for higher education, especially as schools seek to increasingly reach out to underserved populations. In particular, the results of this study show that students attending a minority serving institution do not come to college with the technology skills needed for academic success. Pre and post testing of students, as well as responses to survey questions, have proven the efficacy of computer applications courses at building the technology skills of students. These courses are viewed overwhelmingly positive by students with respondents reporting that they are a necessary part of the college experience that benefits them academically and professionally. Use of an online simulated learning and assessment system with immediate automated feedback and remediation was also found to be particularly effective at building the computer and information literacy skills of students. The total sample size for this study was over 2,800 individuals as data from 2690 IC3 tests administered over a five year period were considered, as well as 160 completed surveys, and pre and post testing of 103 students. Recommendations for Practitioners: Institutions of higher education should invest in a thorough examination of the information and technology literacy skills, needs, and perceptions of students both coming into the institution as well as following course completion. Recommendation for Researchers: This research should be expanded to more minority serving institutions across the United States as well as abroad. This particular research protocol is easily replicated and can be duplicated at both minority and majority serving institutions enabling greater comparisons across groups. Impact on Society: The results of this research help to shed light on a problem that desperately needs to be addressed by institutions of higher education, which is the realities of the digital divide and the under preparedness of entering college students in particular those who are from low income, first generation, and minority groups Future Research: A detailed quantitative survey study is being conducted that seeks to examine the technology uses, backgrounds, needs, interests, career goals, and professional expectations with respect to a range of currently relevant technologies.

Aim/Purpose: To examine how positive/negative message framing – based on peripheral cues (regarding popularity, source, visuals, and hyperlink) – affects perceptions of credibility of scientific information posted on social networking sites (in this case, Facebook), while exploring the mechanisms of viewing the different components. Background: Credibility assessment of information is a key skill in today's information society. However, it is a demanding cognitive task, which is impossible to perform for every piece of online information. Additionally, message framing — that is, the context and approach used to construct information— may impact perceptions of credibility. In practice, people rely on various cues and cognitive heuristics to determine whether they think a piece of content is true or not. In social networking sites, content is usually enriched by additional information (e.g., popularity), which may impact the users' perceived credibility of the content. Methodology: A quantitative controlled experiment was designed (N=19 undergraduate students), collecting fine grained data with an eye tracking camera, while analyzing it using transition graphs. Contribution: The findings on the mechanisms of that process, enabled by the use of eye tracking data, point to the different roles of specific peripheral cues, when the message is overall peripherally positive or negative. It also contributes to the theoretical literature on framing effects in science communication, as it highlights the peripheral cues that make a strong frame. Findings: The positively framed status was perceived, as expected from the Elaboration Likelihood Model, more credible than the negatively framed status, demonstrating the effects of the visual framing. Differences in participants' mechanisms of assessing credibility between the two scenarios were evident in the specific ways the participants examined the various status components. Recommendations for Practitioners: As part of digital literacy education, major focus should be given to the role of peripheral cues on credibility assessment in social networking sites. Educators should emphasize the mechanisms by which these cues interact with message framing, so Internet users would be encouraged to reflect upon their own credibility assessment skills, and eventually improve them. Recommendation for Researchers: The use of eye tracking data may help in collecting and analyzing fine grained data on credibility assessment processes, and on Internet behavior at large. The data shown here may shed new light on previously studied phenomena, enabling a more nuanced understanding of them. Impact on Society: In an era when Internet users are flooded with information that can be created by virtually anyone, credibility assessment skills have become ever more important, hence the prominence of this skill. Improving citizens' assessment of information credibility — to which we believe this study contributes — results on a greater impact on society. Future Research: The role of peripheral cues and of message framing should be studied in other contexts (not just scientific news) and in other platforms. Additional peripheral cues not tested here should be also taken into consideration (e.g., connections between the information consumer and the information sharer, or the type of the leading image).