Hedonic consumption of virtual rides entertainment service: a multi-method perspective: SEM and fsQCA Amir Zaib Abbasi, Farhan Mirza, Mousa Albashrawi, Ding Hooi Ting, Ghazanfar Ali Abbasi Kybernetes, 2025 PurposePrior studies have put much emphasis on using the uses and gratification (U&G) theory to find out why people use games, social media, the Internet, e-shopping, etc. Despite past research efforts, the root causes underlying this phenomenon still need to be discovered as to why people use interactive virtual rides (vrides) entertainment services, especially when incorporating the hedonic consumption perspective (i.e. playful-consumption experiences). Considering the knowledge gap in the vrides’ context, this study aims to use the UGT to find out why people use the vrides entertainment service from a hedonic consumption point of view.Design/methodology/approachWith 217 usable responses, the research model was tested using partial least squares-based structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA).FindingsFindings reveal that only perceived enjoyment, arousal and sensory experience derive continuous intention behavior to consume vride entertainment service. Findings using the fsQCA revealed multiple causal configurations for the proposed outcome.Originality/valueThis study contributes to extending the assumption of UGT via incorporating the hedonic consumption perspective to explore the potential motives and intention to consume vrides entertainment services. Our study also discusses the important theoretical/practical implications of our findings. Besides, this study is unique because it shows both symmetrical and asymmetrical connections that help us understand why people keep using vrides entertainment service.
A comprehensive review of techniques, algorithms, advancements, challenges, and clinical applications of multi-modal medical image fusion for improved diagnosis Muhammad Zubair, Muzammil Hussain, Mousa Ahmad Albashrawi, Malika Bendechache, Muhammad Owais Computer Methods and Programs in Biomedicine, 2025 Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance. This review organizes key knowledge, outlines challenges, and highlights opportunities, guiding researchers, clinicians, and developers in advancing MMIF for routine clinical use and promoting personalized healthcare. To support further research, we provide a GitHub repository that includes popular multi-modal medical imaging datasets along with recent models in our shared GitHub repository.
Time-frequency dynamics of public sentiment transmission in GCC equity markets: evidence from textual analysis Adnan Ameen Bakather, Mohammad Ashraful Ferdous Chowdhury, Mousa Ahmad Al-Bashrawi, Mohammad Abdullah International Journal of Islamic and Middle Eastern Finance and Management, 2025 Purpose This study aims to investigate the time-frequency dynamics of public sentiment transmission in Gulf Cooperation Council (GCC) equity markets, using a novel Twitter-based sentiment index for the GCC stock market. Design/methodology/approach This study constructs a comprehensive Twitter-based sentiment index for the GCC equity market, using 1,107,449 tweets spanning the period 2014–2022. The FinBERT deep learning model is used to perform the sentiment analysis. Moreover, the authors use complete ensemble empirical mode decomposition with adaptive noise-based variable-lag transfer entropy to examine the information flow. Findings The results reveal a significant negative association between the GCC Twitter sentiment index and stock returns in the GCC equity market. The transfer entropy result reveals significant information flow of public sentiment toward the GCC stock market. Moreover, time-frequency analysis indicates that sentiment transmission is more evident in the short- and medium-term scales. Research limitations/implications The study’s findings have significant policy implications, indicating the need for a more complete governance mechanism for GCC stock markets to reduce the impact of irrational investor sentiment on stock returns. The study also emphasizes the need of taking social media sentiment into account when conducting market analysis and making decisions. Originality/value This study adds to the existing literature by introducing a new Twitter-based sentiment index for the GCC stock market and using a novel methodology to investigate the time-frequency dynamics of sentiment transmission.
Generative AI for decision-making: A multidisciplinary perspective Mousa Albashrawi Journal of Innovation and Knowledge, 2025 Generative artificial intelligence (GenAI) is rapidly reshaping decision-making across multiple domains, including health, law, business, education, and tourism. This study synthesizes the fragmented research on GenAI to provide a comprehensive framework for understanding its role in enhancing decision-making accuracy, efficiency, and personalization. Employing a systematic literature review and thematic analysis, this study categorizes diverse applications, from clinical diagnostics and legal reasoning to financial advisement and educational support, highlighting both innovative practices and persistent challenges. The analysis of 101 articles reveals that, while GenAI significantly improves data processing and decision support, mitigating issues such as inherent bias, misinformation, and transparency deficits requires careful attention. The integration of multi-agent frameworks and human oversight is critical for ensuring ethical and reliable outcomes. Ultimately, this synthesis highlights the transformative potential of GenAI as a decision-making tool by presenting a cross-disciplinary framework that reveals its impact and uncovers gaps across various domains. The study also advocates the development of robust regulatory and technological strategies to harness the benefits and address the limitations of GenAI.
Digital Transformation in the IS field: GREAT Research Opportunities in Saudi Arabia and the Gulf Cooperation Council Countries Communications of the Association for Information Systems, 2025
AI Agents and Agentic Systems: A Multi-Expert Analysis Laurie Hughes, Yogesh K. Dwivedi, Tegwen Malik, Mazen Shawosh, Mousa Ahmed Albashrawi, Il Jeon, Vincent Dutot, Mandanna Appanderanda, Tom Crick, Rahul De’, Mark Fenwick, Senali Madugoda Gunaratnege, Paulius Jurcys, Arpan Kumar Kar, Nir Kshetri, Keyao Li, Sashah Mutasa, Spyridon Samothrakis, Michael Wade, Paul Walton Journal of Computer Information Systems, 2025
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“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy Yogesh K. Dwivedi, Nir Kshetri, Laurie Hughes, Emma Louise Slade, Anand Jeyaraj, Arpan Kumar Kar, Abdullah M. Baabdullah, Alex Koohang, Vishnupriya Raghavan, Manju Ahuja, Hanaa Albanna, Mousa Ahmad Albashrawi, Adil S. Al-Busaidi, Janarthanan Balakrishnan, Yves Barlette, Sriparna Basu, Indranil Bose, Laurence Brooks, Dimitrios Buhalis, Lemuria Carter, Soumyadeb Chowdhury, Tom Crick, Scott W. Cunningham, Gareth H. Davies, Robert M. Davison, Rahul Dé, Denis Dennehy, Yanqing Duan, Rameshwar Dubey, Rohita Dwivedi, John S. Edwards, Carlos Flavián, Robin Gauld, Varun Grover, Mei-Chih Hu, Marijn Janssen, Paul Jones, Iris Junglas, Sangeeta Khorana, Sascha Kraus, Kai R. Larsen, Paul Latreille, Sven Laumer, F. Tegwen Malik, Abbas Mardani, Marcello Mariani, Sunil Mithas, Emmanuel Mogaji, Jeretta Horn Nord, Siobhan O’Connor, Fevzi Okumus, Margherita Pagani, Neeraj Pandey, Savvas Papagiannidis, Ilias O. Pappas, Nishith Pathak, Jan Pries-Heje, Ramakrishnan Raman, Nripendra P. Rana, Sven-Volker Rehm, Samuel Ribeiro-Navarrete, Alexander Richter, Frantz Rowe, Suprateek Sarker, Bernd Carsten Stahl, Manoj Kumar Tiwari, Wil van der Aalst, Viswanath Venkatesh, Giampaolo Viglia, Michael Wade, Paul Walton, Jochen Wirtz, Ryan Wright International Journal of Information Management, 2023
Moving to Digital-Healthy Society: Empathy, Sympathy, and Wellbeing in Social Media King Fahd University of Petroleum, Minerals, Mousa Albashrawi, Jongtae Yu, King Fahd University of Petroleum, Minerals, Muhammad Binsawad, King Abdulaziz University, Yousef Asiri, Najran University Pacific Asia Journal of the Association for Information Systems, 2022
AI ADOPTION IN HEALTHCARE: TRUST AND PRIVACY CONCERNS International Conference on Applied Computing 2022 and Www Internet 2022, 2022
Usage behavior of M-banking: An analytical perspective Proceedings of the 24th Pacific Asia Conference on Information Systems Information Systems is for the Future Pacis 2020, 2020
The role of it on entrepreneurial intention: The effect of general computer self-efficacy and computer anxiety Amcis 2017 America S Conference on Information Systems A Tradition of Innovation, 2017
The impact of subjective and objective experience on mobile banking usage: An analytical approach Proceedings of the Annual Hawaii International Conference on System Sciences, 2017
Adoption of mobile ERP in traditional-ERP organizations: The effect of computer self-efficacy Amcis 2016 Surfing the IT Innovation Wave 22nd Americas Conference on Information Systems, 2016
The moderating effect of privacy and personalization in mobile banking: A structural equation modeling analysis 2015 Americas Conference on Information Systems Amcis 2015, 2015
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