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).

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.

Absence of any regular structure is increasingly being observed in structural studies of proteins. These disordered regions or random coils, which have been observed under physiological conditions, are indicators of protein plasticity. The wide variety of interactions possible due to the flexibility of these 'natively disordered' regions confers functional advantage to the protein and the organism in general. This concept is underscored by the increasing proportion of intrinsically unstructured proteins seen with the ascension in the complexity of the organisms. The 'natively unfolded/disordered' state of the protein can be predicted utilizing Uversky's or Dunker's algorithm. We utilized Uversky's prediction scheme and based on the unique position of a protein in the charge-hydrophobicity plot, a derived net score was used to predict the overall disorder of the human housekeeping and non-housekeeping proteins. Substantial numbers of proteins in both the classes were predicted to be unfolded. However, comparative genomic analysis of predicted unfolded Homo sapiens proteins with homologues in Caenorhabditis elegans, Drosophila melanogaster and Mus musculus revealed significant increase in unfoldedness in non-housekeeping proteins in comparison with housekeeping proteins. Our analysis in the evolutionary context suggests addition or substitution of amino acid residues which favour unfoldedness in non-housekeeping proteins compared to housekeeping proteins.