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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.

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.

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.

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.

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.

In the current dynamic business environment, Internet of Things (IoT) is employed by a number of companies in the logistics industry to achieve intelligent sorting, network optimization, real-time tracking and simplifying last-mile service. Although logistics entities are trying to introduce IoT into their business areas, users' perception of this new technology is still limited. This paper aims to develop a research model for the factors influencing the user adoption of IoT technology in the logistics industry.

In this study, based on the major theories on the application of new technologies such as technology acceptance model (TAM), technology–organization–environment (TOE) and innovation diffusion theory (IDT), a new research model was established to identify factors affecting customers' behavioral intention (BI) to adopt IoT technology provided by logistics companies. In addition, the authors surveyed unspecified customers of Cainiao Logistics Network, which is in charge of the logistics operation of Alibaba Group, China's largest e-commerce company, and tested the causality between the latent variables presented in the model using the structural equation model (SEM).

This empirical study shows that the support system of a logistics company and users' innovative propensity significantly affect perceived ease of use (PEOU) and BI for logistics services to which IoT technology is applied. It also presents that users' perceived security and enjoyment significantly affect perceived usefulness (PU) and BI. In addition, it was possible to confirm that the causal structure between variables suggested by TAM that PEOU has a significant effect on PU and BI, and PU has a substantial effect on BI.

Logistics companies should expand and upgrade technical support systems so that customers can flexibly accept logistics services with IoT technology and make efforts to alleviate customers' concerns about personal information leakage. In addition, it is necessary to find customers with an inclusive attitude toward using new technologies, to induce them to become leading users of logistics devices with IoT technology and to find various ways to amplify their enjoyment. Through a strategic approach to these technical and individual factors, it will be possible to boost customers' intention to use IoT logistics services.

As far as the authors know, this paper is the first study to set significant factors that affect users' BI to use IoT technology-applied logistics services provided by logistics companies and empirically analyze the causal relationships between proposed latent variables.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Lack of access to finance is a major constraint to the growth of small and medium-sized enterprises (SMEs) and entrepreneurship in developing countries. The recent proliferation of mobile phone services, access to the internet and emerging technologies has led to a surge in the use of FinTech in Africa and is transforming the financial sector. This paper aims to examine whether FinTech developments heterogeneously contribute to the growth of digital finance for SMEs and entrepreneurship in 47 African countries from 2013 to 2020.

The paper uses a novel method of moments quantile regression, which deals with heterogeneity and endogeneity in diverse conditions for asymmetric and nonlinear models.

The empirical results reveal that the rise of FinTech companies offering services in Africa heterogeneously increases digital finance for SMEs and entrepreneurship in their different stages of growth. FinTech developments have a strong and positive impact in countries with higher levels of digital finance than those with lower levels. FinTech developments and digital finance positively and significantly influence entrepreneurship in Africa, particularly in the nascent and transitional development stages of entrepreneurship. Institutional quality has a considerable positive moderating effect when used as a control rather than an interaction variable.

The results suggest the need to promote FinTech developments in Africa: to provide a wide range of alternative digital finance schemes to SMEs and to promote entrepreneurship, especially in countries where entrepreneurship is in the nascent and transitional development stages. The results also underscore the need to promote FinTech development through supportive regulations and institutional quality to reduce risks related to FinTech and digital financing schemes.

To the best of the authors’ knowledge, this paper is one of the first attempts to account for the often overlooked heterogeneity effects and show that the influence of FinTech developments is not homogenous across the varying development stages of digital finance and entrepreneurship.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

This paper has a dual purpose. The first is to provide a thorough analysis of developments in international marketing in relation to the coronavirus disease 2019 (COVID-19) pandemic; the second is to capitalize on these developments to set an agenda for future research in the field of international marketing.

This paper zooms in on and reviews the 18 papers published in International Marketing Review’s (IMR) Special Issue on “Covid 19: advancing international marketing theory and guiding practice” (2023, volume 40, issue 5). It also integrates recent research at the intersection of international marketing and the COVID-19 pandemic.

The paper highlights five areas that embody significant contemporaneous changes brought about by the COVID-19 pandemic and affect international marketing practice. These include (1) shifts in consumer behavior, (2) digitalization and artificial intelligence, (3) disruptions in supply chains, (4) communication and corporate social responsibility (CSR), and (5) international dynamic marketing capabilities. In order to advance international marketing theory in relation to pandemics and other external crises, the paper establishes research directions for each of these areas.

The paper provides a novel and comprehensive categorization of fundamental shifts caused by the COVID-19 pandemic and lays out a research roadmap to advance research in the field of International Marketing (IM). Important implications for practice are also discussed.

India is a fast-growing economy, that has a majority share in the global information technology industry (IT). Rapid urbanisation and modernisation in India have strained its energy sector, which is being reformed to cope. Despite being the global IT heart and having above average research output in the field of artificial intelligence (AI), India has not yet managed to leverage its benefits to the full. This study aims to address the role of AI and information management (IM) in India’s energy transition to highlight the challenges and barriers to its development and use in the energy sector.

The study, through analysis of proposed strategies, current policies, available literature and reports, discusses the role of AI and IM in the energy transition in India, highlighting the current situation and challenges.

The results show dispersed research and development incentives for IT in the Indian energy sector; however, the needed holistic top-down approach is lacking, calling for due attention in this matter. Adaptive and swift actions from policymakers towards AI and IM are warranted in India.

The ongoing transition of the Indian energy sector with the integration of smart technologies would result in increased access to big data. Extracting the maximum benefits from this would require a comprehensive AI and IM policy.

The revolution in AI and robotics must be carried out in line with sustainable development goals, to support climate action and to consider privacy issues – both areas in India must be strengthened.

The paper offers an original discussion on certain applicable solutions regarding the energy transition of AI coming from the Global South; they are based on lessons learned from the Indian case studies presented in this study.

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.

The purpose of this article is to develop a configurational approach based on the TOE framework (technology, organization and environment) to understand the degree of implementation of I4.0 technologies in manufacturing small- and medium-sized enterprises (SMEs). Specifically, the study considers technological infrastructure and competence, I4.0 integration capabilities, organizational agility and strategic flexibility, environmental dynamism and industry-specific forces as simultaneous pre-conditions for achieving an effective implementation of I4.0 technologies.

This study uses the fuzzy-set qualitative comparative analysis (fsQCA) methodology as it allows for asymmetric and configurational-focused testing of proposition and sound theoretical development. In total, 305 responses were collected through a survey administered to SME managers in Europe and the United Kingdom (UK).

The study examines the influence of technology, organizational and environmental aspects on I4.0 technologies implementation in SMEs. High I4.0 degree of implementation is structured around 5 configurations, while other 4 configurations are related to low levels of I4.0 implementation.

This study proposes a configurational approach for SMEs to become I4.0 ready and how they may successfully implement I4.0 technologies. Such findings represent an original and novel contribution to existing research, offering a broad view on the I4.0 implementation by manufacturing SMEs.

The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.