Many challenges continue to hinder digital technologies' adoption by small and medium scale enterprises (SMEs) in developing economies. Comparatively, there are more success stories by SMEs in emerging markets. However, most SMEs operating in the informal sector in the emerging markets and developing economies (EMDEs) face similar challenges that inhibit the adoption of advanced technologies and innovations needed to improve business operations and re-engineer processes. This article evaluates the implementation and use of state-of-the-art technologies by SMEs in EMDEs to improve operations performance and create sustainable competitive advantages. Further, the papers in this Special Issue identify FinTech and analytical algorithms as some of the current technologies employed by SMEs in EMDEs to improve operations and processes in the manufacturing and service industries. The recognized technologies and technical innovations that seem novel in EMDEs have long existed in the advanced economies. Most state-of-the-art technologies, including cloud computing, 'big data', and predictive analytics that can improve operations and strategic decisions, are yet to make inroads in most EMDEs. Also, disruptive computing technologies, data analytics, and the Internet of Things (IoT) required to engineer new business models, reduce overheads, enhance competitive advantages, and digitize SMEs' business operations remain untapped. The absence and non-adoption of digital technologies in EMDEs explain why business activities in most EMDEs remain shut during the outbreak of SARS-CoV-2 and the community lockdown to contain the COVID-19 pandemic. The strategies to survive the 'new normal' imposed by COVID-19 and fierce global competition includes a successful adoption of advanced technologies.

The adoption of cutting-edge technologies to steer business activities during community lockdown to contain the spread of the COVID-19 pandemic, even if involuntarily, provides evidence that technologies not only offer competitive advantages but also provides a means for survival, by improvising existing business models. In June 2019, we issued a call for papers to address the awareness, adoption, and implementation challenges of technologies that can drive businesses of all sizes in the fourth industrial revolution. We intended to identify as critical elements the “must-have” and a “nice to have” technologies for small businesses and innovation. Then the ongoing COVID-19 global health pandemic struck in December 2019, forcing the need for digitization of business activities and remote operations, which was considered a “nice to have” to immediately become a “critical to have” to survive in the ever increasingly uncertain business environment. This paper identifies the technologies, evaluates disruptive software platforms, and strategies needed for creating and managing small business innovation and highlighting the complexity of that process and the context within which this process takes place. We integrate this discussion alongside a summary of the articles included in the Special Issue. The current realities show that technologies that enable social business creation, customer relationship management systems, new communications channels, virtual reality technologies for remote operations, and the Internet of Things (IoT) are crucial to lowering the costs of doing business. Big data and predictive and visual analytics are critical enablers to aiding complex business decisions in the current challenging business climate.

Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

Research on the application of 3D visualization and virtual reality (VR) in discrete-event simulation (DES) has received increased attention in the past two decades. The increasing popularity of the 3D display in DES is mainly due to superior display capabilities and the associated benefits that it offers. However, the 2D display also continues to enjoy active use to date, thus provoking some fierce debates questioning the need for the 3D and VR if the 2D interface suffices. Several studies comparing the effectiveness of the different visualization methods also produce different conclusions. This paper undertakes a meta-analysis of the different positions and synthesizes the findings from 162 studies on the impacts of the 2D display versus 3D/VR on user performance on various DES tasks. The results highlight four key findings. First, the perception that the 2D display is more effective for model development is misleading as 3D/VR offers overall better performance and quality of models. Second, 3D/VR enables more effective performance than 2D display for model verification and validation. Third, 3D/VR decreases the time taken for verification, validation, experimentation, and analysis of results, but can increase model development time. Finally, the latent variables such as the application domains and nature of the problems tackled have no direct or indirect influence on the efficacy of the 3D display/VR versus 2D on DES task performance.