As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

Purpose: The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.Design/methodology/approach: Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.Findings: The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.Practical implications: This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.Originality/value: This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

Purpose: This paper reviews the extant research on Web3.0 published between 2003 and 2022.Design/methodology/approach: This study uses a topic modeling procedure latent Dirichlet allocation to uncover the research themes and the key phrases associated with each theme.Findings: This study uncovers seven research themes that have been featured in the existing research. In particular, the study highlights the interaction among the research themes that contribute to the understanding of a number of solutions, applications and use cases, such as metaverse and non-fungible tokens.Research limitations/implications: Despite the relatively small data size of the study, the results remain significant as they contribute to a more profound comprehension of the relevant field and offer guidance for future research directions. The previous analysis revealed that the current Web3.0 technology is still encountering several challenges. Building upon the pioneering research in the field of blockchain, decentralized networks, smart contracts and algorithms, the study proposes an exploratory agenda for future research from an ecosystem approach, targeting to enhance the current state of affairs.Originality/value: Although topics around Web3.0 have been discussed intensively among the crypto community and technological enthusiasts, there is limited research that provides a comprehensive description of all the related issues and an in-depth analysis of their real-world implications from an ecosystem perspective.

This paper examines existing Web3.0 research published between 2003 and 2022. We uncover seven research themes that have been featured in previous research, as well as the key phrases associated with each theme, using a topic modeling procedure called Latent Dirichlet Allocation (LDA). We emphasize the interaction of research themes that contribute to the understanding of a variety of solutions, applications, and use cases, such as metaverse and Non-fungible tokens (NFT). We conclude by proposing an exploratory agenda for future research based on pioneering research in the fields of Blockchain, decentralized networks, smart contracts, and algorithms.