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Small and Medium-sized Enterprises (SMEs) are regarded as the backbone of the growth of the world economy. They had recently experienced rapid growth and improved their business activities in terms of customer numbers and revenue expansion when they began using e-commerce in their business. Although there is a growing interest in e-commerce, its use is still insufficient in Algeria. SMEs show a little acceptance of e-commerce in their business processes. Consequently, e-commerce has not yet found its place in Algeria, since it has not achieved the desired start as an approved means of trade. The main objective of this study is to identify the direct, indirect and mediating factors that have prevented SMEs from adopting e-commerce in Algeria and develop a model to be adopted. To obtain a clear vision on the subject of adopting e-commerce in Algeria, a preliminary study was established with semi-structured interviews that were conducted with 7 SME managers. Then, the conceptual model was developed through the models and theories which are among the most frequently applied theoretical models on the adoption of e-commerce in developing countries. These models are Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Organisation Environment (TOE), and Perceived eReadiness Model (PERM) as well as from the results of the preliminary study. This research uses the quantitative research method, where a survey responded by 315 SMEs in Algeria. The research hypotheses were examined, and the proposed research model was validated through Structural Equation Modelling (SEM) using AMOS software. The main findings of the present study comprise some key factors that have a significant effect on e-commerce adoption which are competitive pressure, delivery systems, necessary guidance and assistance, buying habits, enterprise financial resource, human resources, and trust in the state system and IT skills. The significant effects were mediated by three variables which are awareness, fear of risk in e-commerce, and the intention to adopt e-commerce. At the same time, there are three non-significant factors which are government e-readiness, bank e-readiness and technology resources. Besides, the measures of goodness-of-fit indices GOFI indicate that the conceptual model revised is a fit model for e-commerce adoption by SMEs in Algeria. This model would be provided useful information for SMEs, policymakers, and academics.

The fourth pillar of the United Nations Sustainable Development Goals is 'Quality education' to provide peace and prosperity to the world population; in addition, Special Education Reforms under the Malaysian government scope are influenced by the 'Shared Prosperity Vision 2030'. Special needs education underlines the importance of technological integration in the learning environment to support communication, teaching, and learning needs. Technological advancements assist educators in transforming the educational needs of neurodiverse learners at all academic levels. The early intervention-based teaching and learning process is advocated worldwide, as it has shown positive cues toward neurodiverse learners, especially those with Autism Spectrum Disorder (ASD). However, very little is known about the perception and behavior of teachers toward acceptance of technology-based intervention. Therefore, to bridge the identified gap in academic literature, this study explores the teachers' behavioral intentions concerning the use of technology for teaching children with ASD. A conceptual framework has been developed based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and other recent studies to address the problem. In this study, constructs such as Performance Expectancy (PE), Personal Innovation (PI), Effort Expectancy (EE), Habit (H), Social Influence (SI), Time (T), Access to Technology (ATT), Voluntariness (V), Self-Efficacy (SE), Facilitating Conditions (FC), Job Relevance (JR), Hedonic Motivation (HM), Risk (RK), and Trust (TR) are measured. The researcher later tested the hypotheses through a survey-based self-administered questionnaire among special education teachers in different parts of Malaysia. This study's theoretical model was evaluated using two different techniques. The first used the PLS-SEM and the Smart-Partial Least Squares software tool. Structural equation modeling was used to evaluate hypothetical relationships. In the second technique, validating hypotheses, assessing their methodology, and gauging their prediction capacity are all tasks that benefit greatly from the application of Machine Learning. In order to look into the correlational routes between constructs, a quantitative research approach of cross-sectional survey method was adopted. A total of 386 replies were compiled using a cluster sampling strategy. 12 out of 17 hypotheses were determined to be statistically significant, and five were found to be insignificant, according to research findings. The findings showed that the following variables had a significant impact on teachers' behavioral intention (BI) to adopt technology for teaching children with ASD and their use behavior (UB): PE, EE, H, ATT, SE, FC, JR, HM, RK, and TR. Whereas BI, H, and FC explain use behavior. The study's findings demonstrated that the suggested model matched the data correctly. Additionally, this study predicts and validates the UTAUT2 model in the context of technology adoption among the teachers of children with ASD, expanding the applicability of the UTAUT2 in IS research. The ML generic algorithms predicted the conceptual model with the best results for the Random Forest algorithm, R2 value for the training (0.993) and testing (0.971) with a minimum error of RMSE = 0.1729 and MAE = 0.1239, in comparison to the predictive power of TAM (R² = 0.67) and UTAUT (R² = 0.73). Finally, this research added to the understanding of technology acceptance among teachers responsible for teaching children with ASD. The study's identification of crucial elements would influence stakeholders, school administrators, and policy and lawmakers to design and implement an inclusive special needs education system and use technology effectively to teach children with ASD, particularly in Malaysia.

Vaccination has been proven to be an effective measure to prevent the spread of harmful diseases. Despite its efficacy, the moves towards vaccine hesitancy have been receiving global attention. Vaccine hesitancy issues have been openly discussed across major social media platforms including Facebook, Reddit, Twitter, Instagram and YouTube. The spread of vaccine hesitancy-related posts is propagated substantially, causing greater threats to public health. Consequently, various state-of-the-art machine learning techniques have been proposed to analyse vaccine-hesitant related posts in social media. One of the most recent approaches is the transfer learning method using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model. Despite vaccine hesitancy being a prevalent issue across multiple social media platforms, only a few studies have utilised data from multiple social media platforms to detect vaccine hesitancy. To address this research gap, the use of BERT as one of the new language representation models is adopted to train from a collection of vaccine hesitancy related data from multiple social media platforms. Moreover, this study employs the Support Vector Machine (SVM) and Logistic Regression (LR) models and compare their performances against the BERT method. The objectives of this research are threefold; to establish a consolidated dataset from multiple social media sources for use in vaccine hesitancy detection, to evaluate the effectiveness of using mono-platform versus multi-platform vaccine hesitancy data on the performance of different machine learning models and to apply a transfer learning method using BERT in vaccine hesitancy detection. A collection of 193,023 labelled vaccine hesitant posts were aggregated from three (3) social media platforms which includes Facebook, Reddit, and Twitter. The results demonstrate that the BERT model performs the best and achieved an F1-score of 0.93, while both the SVM and LR achieved F1-scores of 0.90 when detecting vaccine hesitancy from multiple social media platforms. Our proposed research also revealed that models trained with multi-platform data perform at least 15% better than models trained with mono-platform data when tested with multi-platform data.

Most contemporary research on stress focuses on its negative appraisal (distress) while its benefits (eustress) have relatively been ignored or relied on self-reported data. These constructs, known as ‘core affect’, can be represented on a two-dimensional (x-y) axis where the x-axis (valence) depicts positive to negative emotions and the y-axis (arousal) depicts drowsiness to excitedness. The first phase of this study examines the relationship between stress appraisal and emotional states in an academic environment. Feedback to an online Google Forms survey consisting of various psychological instruments for assessing stress and affect, was collected. Based on significant correlations between scores for the stress questionnaires and scores for the affective scale, prediction equations were derived. Mean squared error (MSE) performance evaluation showed that the model for Academic eustress had the lowest loss function while the model for ADES eustress had the lowest percentage error. In the second phase of the study, electroencephalography (EEG) signals were collected from the prefrontal cortex region of participants while they viewed happy, fear, calm and sad emotive images from the International Affective Picture System (IAPS) database. Bandpass filtering was used to separate signals according brainwaves while Independent Component Analysis (ICA) was used for artefact removal. Mel Frequency Cepstral Coefficient (MFCC) features are then extracted from brainwaves and fed into a Multilayer Perceptron (MLP) that classifies emotion based on valence and arousal. A memory test was then used to validate the model, with a set accuracy threshold. Participants below the threshold were excluded, with the final model called the neurophysiological computational model of affect (NCMoA), which is used to extract valence and arousal from EEG. Pearson correlation analysis is used to assess the relationship between extracted valence and arousal scores and stress questionnaire scores. Prediction equations were derived, with the model for ADES eustress having the lowest loss function and the model for PSS-10-C having the lowest percentage error. These valence and arousal are extracted from EEG signals of participants as they solved arithmetic tasks of low, medium and high relative difficulty. The emotion and stress scores showed significant correlations and prediction equations were again derived. ADES distress had the lowest loss function. Finally, ADES questionnaire produced models for core affect during arithmetic tasks. So, it was used to determine the boundaries for the minimum and maximum eustress. In summary, this study sheds light on strategies of coping with stress and recognizing its triggers. This provides a pathway to effective stress management.

University libraries are transitioning towards a digital landscape, offering various electronic information services to support student research and learning. Understanding factors influencing customer satisfaction with these services is critical for optimizing library resources and fostering user engagement. This study investigates the impact of e-marketing strategies and technology turbulence on customer satisfaction with electronic library services in Jordanian universities. The research framework integrates the Resource-Based View (RBV) theory, emphasising the importance of internal resources for achieving customer satisfaction, and the 7Ps of the e-marketing mix framework, representing these resources within university libraries. Additionally, contingency theory highlights the need for libraries to adapt to external factors like technology turbulence, which can influence the effectiveness of e-marketing strategies. A mixed-methods approach was employed, utilising qualitative data from semi-structured interviews with eight library managers and quantitative data from a survey of postgraduate students at Jordanian public universities (792) who were selected using a voluntary response sample. The qualitative phase identified current e-marketing practices in Jordanian university libraries. The quantitative phase examined the relationships between the 7Ps of the e-marketing mix (e-product, e-pricing, e-place, e-promotion, people, process, and physical evidence) and customer satisfaction with electronic services. Furthermore, the study explored the moderating effect of technology turbulence on these relationships using structural equation modelling (SEM) (Smart PLS 3.0). The findings revealed that several e-marketing components significantly impact customer satisfaction. Specifically, well-designed e-products (e.g., databases, online tutorials), effective e-promotional activities, knowledgeable and helpful library staff, and clear and accessible service information (physical evidence) were all contributing factors. However, e-place and the process had a negative impact, and e-pricing was found to be non-significant in its direct effect. Additionally, the interactions between technology turbulence and e-products, e-promotions, people, and processes were non-significant. In contrast, the research identified that e-place had a negative impact and significant interactions between technology turbulence, e-pricing, and physical evidence. This research confirms the novelty of integrating e-marketing strategies in the university library context, so this research offers a novel framework for Jordanian university libraries to enhance student satisfaction with electronic services. The study contributes valuable insights for library managers and policymakers, enabling them to develop targeted strategies for promoting electronic information services and fostering a user-centric approach. Furthermore, the research adds to the existing knowledge base on e-marketing in the university library sector, particularly within the Arab region, especially in the Jordanian context, where this area remains relatively under-explored. Therefore, more empirical studies are needed to investigate and explore this gap in the research. The study's limitations, including the use of a voluntary response sample and the focus on Jordanian universities, highlight the need for further research to explore the generalizability of these findings across a broader population and geographical scope. Future research directions could involve investigating the long-term impact of e-marketing strategies on student learning outcomes and exploring integrating emerging technologies like artificial intelligence into the library service ecosystem.

E-learning enables an environment in which educational activities are delivered through online platforms using computing devices to empower learning facilities anywhere and anytime without geographical boundaries. During the COVID-19 crisis where social distancing was mandatory, many e-learning platforms were adopted by institutions around the globe. Experiences show that for the adaptation of E-learning, several challenges were faced by educational institutions, among them some of the core challenges are Cost, System Quality, System Complexity, Infrastructure, Social influence, COVID-19 effect, and Student learning motivation. Therefore, this research aims to find the factors affecting the adoption of E-Learning during COVID-19 in Pakistan. To achieve this, a proposed hypothetical model has been formed based on well-known IT/IS acceptance models such as extended TAM, UTAUT, M&D, TRA, and IDT. Keeping problems in mind, an extensive literature search has been completed. In this study, constructs like Perceived Cost, System Quality, Complexity, Social Influence, Facilitating Condition, COVID-19 (Lockdown and COVID fear); Student learning motivation; Perceived Ease of User (PEOU), and Perceived Usefulness (PU) are the most significant variables to impact Behavior Intention (BI) to use e-learning in Pakistan, are measured. The hypothesized correlations were investigated using Smart-PLS software and Structural Equation Modeling (SEM). The correlational paths between constructs are studied using a quantitative methodology (survey questionnaire). A cluster sampling methodology was employed to gather a comprehensive dataset of 461 responses from students who utilized e-learning throughout the COVID-19 epidemic. The research findings indicate a significant statistical relationship in 13 out of the 15 hypotheses tested, while the other 2 did not yield statistically significant results. The research findings suggest that a significant portion, specifically 61.1%, of the change in Behavioral Intention may be reported by factors such as PU, PEOU, SI, SQ, CS, and COVID-19. Likewise, PEOU factors explained 39% of the variance, and Perceived Usefulness explained 54.7% in descending order of importance. Furthermore, the Cost and COVID-19 factors involved in adopting e-learning during COVID-19 negatively correlate with behavioral intention. Thus, Cost and COVID-19 were considered obstacles to adopting e-learning in the COVID-19 pandemic. This study contributes to identifying the significant factors influencing the behavioral intentions of Pakistani students for e-learning adoption during the COVID-19 pandemic, it also proposes an integrated research model to investigate the adoption of e-learning. Moreover, it offers practical and theoretical implications of its findings for stakeholders, including policymakers, educators, researchers, and industry practitioners involved in developing and implementing e-learning initiatives in Pakistan.