The Arabic language presents unique modeling challenges, such as its morphological complexity, orthographic ambiguity, dialectal variations, and orthographic noise. Furthermore, the scarcity of linguistic resources dedicated to Arabic and the limited availability of detection tools compound the difficulties in effectively identifying and mitigating offensive language in Arabic text. Given Arabic's linguistic richness and diversity, it is essential to develop robust and accurate methods for detecting and addressing offensive language to ensure the safety and well-being of Arabic-speaking online communities. This study aims to enhance the performance of Arabic offensive tweet classification by proposing a novel framework combining advanced preprocessing techniques and state-of-the-art classification models in an ensemble methodology. It comprises two primary modules: preprocessing module and classification module. The preprocessing module incorporates AraBERT preprocessing, emoji-to-word interpretation, and punctuation removal. The classification module encompasses neural networks (NN), LSTM, and AraBERT. The proposed ensemble model combines a fine-tuned AraBERT with two NN layers, LSTM, ReLU, and Sigmoid activation functions. The model achieved state-of-the-art performance, surpassing all other approaches in terms of Accuracy and F1-score. These results highlight the effectiveness and potential of leveraging advanced models like AraBERT in detecting and classifying offensive language in Arabic text.

Telemedicine offers transformative solutions for healthcare delivery, particularly in bridging geographical barriers and enhancing patient care. Nevertheless, its adoption by physicians is intricate, driven by diverse factors that require systematic understanding. This systematic review aims to identify and classify the factors influencing physicians’ adoption of telemedicine. From a collection of 2098 articles sourced from Web of Science and Scopus databases, 59 studies met the predetermined criteria and were meticulously assessed. The salient factors from these chosen papers were segmented into distinct categories: psychological and behavioral factors, technological factors, social and cultural factors, security and privacy factors, conditional factors, quality factors, healthcare-related factors, and inhibiting factors. Building on this classification, we proposed a comprehensive framework for telemedicine adoption tailored to physicians. This framework endeavors to act as a scaffold for subsequent empirical investigations. The taxonomy of the factors also aided in offering numerous research agendas to pave the way for more exhaustive studies into telemedicine adoption among medical professionals. The insights procured serve not only to deepen theoretical comprehension but also to present pragmatic guidelines for practitioners, service providers, application developers, and decision-makers.

The rapid emergence of Generative Artificial Intelligence (GAI) heralds a significant shift, opening new frontiers in how education is delivered. This groundbreaking wave of technological advancement is poised to redefine traditional learning, promising to enhance the educational landscape with unprecedented levels of personalized learning and accessibility. Despite GAI’s progressive infiltration into various educational strata, limited empirical research exists on its impact on students’ learning performance. Drawing on the Theory of Planned Behavior (TPB) and Behavioral Reasoning Theory (BRT), this study investigates the determinants affecting students’ use of ChatGPT and its influence on learning performance. The data were collected from 357 university students and were analyzed using the PLS-SEM technique. The results supported the role of ChatGPT in positively affecting students’ learning performance. In addition, the results showed that reasons for and against adoption are pivotal in shaping students’ attitudes. ChatGPT use is found to be significantly affected by attitudes, subjective norms, and perceived behavioral control. Besides the theoretical contributions, the findings offer various implications for stakeholders and underscore the necessity for educational institutions to foster a conducive environment for GAI adoption, addressing ethical and technical concerns to optimize learning experiences.

The Metaverse, an expansive online 3D virtual realm, has transformed how people work, socialize, and engage, but its growth brings critical cybersecurity challenges, particularly in data security and privacy. Since many cybersecurity incidents stem from human behavior, understanding factors influencing users’ cybersecurity practices in the Metaverse is vital. This study addresses the gap by proposing an integrated model based on Protection Motivation Theory (PMT), Health Belief Model (HBM), and Theory of Interpersonal Behavior (TIB), with trust as an additional construct. Using a hybrid structural equation modeling-artificial neural network (SEM-ANN) approach, data from 531 Metaverse users were analyzed. The results indicated that perceived vulnerability, self-efficacy, cues to action, habit, and trust significantly affect cybersecurity behavior, with “cues to action” identified as the most critical factor (97.8% normalized importance). Conversely, perceived severity, response efficacy, response costs, and facilitating conditions showed no significant impact. The findings offer theoretical insights and practical implications for Metaverse stakeholders and cybersecurity strategies.

The Metaverse is rapidly transforming virtual interactions, especially in education, but its growth also attracts cyber threats. Without understanding and addressing users’ cybersecurity behaviors, the Metaverse’s full potential is at risk, making investigating these behaviors a pressing necessity. Grounded on the theory of planned behavior (TPB), technology threat avoidance theory (TTAT), and protection motivation theory (PMT), this research develops an integrated theoretical model to evaluate users’ cybersecurity behaviors in the Metaverse. Data were gathered from 701 Metaverse users and were analyzed using a hybrid structural equation modeling-artificial neural network (SEM-ANN) approach. Of the 11 proposed hypotheses, the Partial Least Squares-Structural Equation Modeling results showed that nine were supported, explaining 63.1% of the variance in cybersecurity behavior. The ANN analysis revealed that avoidance motivation and attitude are the most significant factors influencing cybersecurity behavior. In addition to its theoretical contributions, the findings offer actionable insights for various stakeholders.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

Despite social networks’ prevalence and unlimited benefits, their adoption rates are still unsatisfactory. This cross-country research aims to examine the impact of network externality (NE) and fear of missing out (FOMO) on the continuous use of social networks, which in turn, affects users’ self-esteem. To achieve this aim, a conceptual model is developed by extending the unified theory of acceptance and use of technology 2 (UTAUT2) with three new factors: NE, FOMO, and self-esteem. The model is tested using a quantitative research design based on data collected through online surveys from 841 social media users in Qatar and Jordan. The data were analyzed using partial least squares-structural equation modeling (PLS-SEM). The results indicated that the continuous use of social networks is positively affected by performance expectancy (PE), hedonic motivation (HM), and FOMO in both samples. The continuous use is also affected by effort expectancy (EE) in the Jordanian, but not the Qatari sample. In contrast, NE significantly affects the continuous use among Qatari respondents, while this relationship is not supported among their Jordanian counterparts. More interestingly, the continuous use of social networks positively impacts users’ self-esteem across the two samples. In summary, this research goes beyond what was examined in the UTAUT2 by investigating the consequences of continuous use on users’ self-esteem. The incorporated constructs extend the theoretical perspective of the UTAUT2 by integrating new determinants of the continuous use (i.e., FOMO and NE) and new outcomes of that use (i.e., self-esteem). The reflection of the impact of these factors in a cross-country comparison provides insights into the variation in using social networks between different countries.

Autonomous vehicles (AVs) offer several benefits, such as improving road safety, mitigating traffic congestion, and reducing fuel consumption and gas emissions. Despite these benefits, their adoption rate remains limited due to various factors influencing users’ decisions. While previous studies have identified numerous factors influencing AV adoption using various adoption frameworks, the factors have not been comprehensively analyzed and synthesized. Thus, this systematic review aims to bridge this gap by identifying and classifying the factors influencing the adoption of AVs. Out of 3,532 collected research papers, 71 empirical studies were analyzed thoroughly. The findings demonstrated that the technology acceptance model (TAM) was the most widely used model for investigating AV adoption. The identified factors in the analyzed studies were classified into distinct categories: psychological and behavioral factors, technological factors, social factors, environmental factors, security and privacy factors, AV-related factors, risky and negative factors, conditional factors, and monetary factors. We have proposed an AV adoption framework grounded in this taxonomy to direct subsequent empirical research. We have also highlighted numerous agendas to serve as a blueprint for future AV adoption studies. This review offers various theoretical insights and actionable recommendations for multiple AV research, development, and implementation stakeholders.

The Metaverse holds immense potential for individuals, organizations, and society, providing immersive and innovative experiences. However, its adoption is a complex process influenced by various factors yet to be thoroughly understood. Therefore, this systematic review aims to identify and classify the drivers and barriers affecting Metaverse adoption. Of the 279 papers gathered from the Web of Science and Scopus databases, 29 studies fulfilled the eligibility requirements and underwent a detailed analysis. The identified factors in the selected studies were classified into distinct categories, including motivational and psychological factors, social factors, technological and Metaverse-related characteristics, learning experience-related factors, inhibitors, privacy and security factors, conditional factors, quality factors, economic-related factors, and personalization and immersion-related factors. We have then proposed a comprehensive Metaverse adoption framework based on this taxonomy to guide future empirical studies. We have also suggested several agendas as a road map for future research on Metaverse adoption. Based on these findings, the review presents several theoretical contributions and practical implications for Metaverse developers and marketers.

Virtual meeting platforms have been identified as the golden bullet to deliver the learning materials to students during the COVID-19 pandemic. While this is evident across thousands of universities across the globe, the literature is scarce on what impacts the continued use of these platforms during and beyond the COVID-19 pandemic. Therefore, this research develops a theoretical model to examine the impact of psychological, social, and quality factors on the continuous intention to use these platforms. Unlike the previous adoption studies, which mainly relied on structural equation modeling (SEM) analysis, the developed model was validated through a hybrid approach using SEM and artificial neural network (ANN) based on data collected from 470 students. The hypotheses testing results indicated that psychological, social, and quality factors have significant positive impacts on the continuous intention to use virtual meeting platforms. The sensitivity analysis results revealed that psychological factors have the most considerable effect on the continuous intention to use virtual meeting platforms with 100% normalized importance, followed by quality factors (72%), and social factors (31%). The contribution of this study lies behind the development of an integrated model that considers the psychological, social, and quality factors in understanding the continuous intention to use virtual meeting platforms during and beyond the COVID-19 pandemic.

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

Despite the growing literature on employing smartwatches for communication and healthcare services, there is little debate on how to use these wearables for educational purposes. Therefore, this research develops a hybrid theoretical model through the integration of the Technology Acceptance Model (TAM), Task-technology Fit (TTF) variables, and the most quality features of smartwatches, namely availability and mobility in order to explore the students’ behavioral intention to adopt smartwatches for learning activities. The developed model was validated through data collected from 275 university students using the partial least squares-structural equation modeling (PLS-SEM) technique. The empirical results indicated that individual-technology fit and task-technology fit positively impact the perceived usefulness of smartwatches, while no positive effects were reported on the ease of use of these wearables for educational purposes. The results also revealed that availability and mobility positively affect the perceived ease of use of smartwatches for instructional activities. The theoretical contributions and practical implications of these results were also tackled.