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Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series. The initial history, published in 2012 for the 30th Anniversary Volume, describes key events in the history of the series and provides information about key authors and contributors to Advances in Econometrics. The authors update the original history and discuss significant changes that have occurred since 2012. These changes include the addition of five new Senior Co-Editors, seven new AIE Fellows, an expansion of the AIE conferences throughout the United States and abroad, and the increase in the number of citations for the series from 7,473 in 2012 to over 25,000 by 2022.

Advances in Econometrics is a series of research annuals first published in 1982 by JAI Press. In this paper, we present a brief history of the series over its first 30 years. We describe key events in the history of the volume, and give information about the key contributors: editors, editorial board members, Advances in Econometrics Fellows, and authors who have contributed to the great success of the series.

The paper aims to carry out a comprehensive literature mapping to synthesise and descriptively analyse the research trends of biodiversity accounting, providing implications for managers and policymakers, whilst also outlining a future agenda for scholars.

A bibliometric analysis is carried out by adopting the Preferred Reporting Items for Systematic Review and Meta-Analyses protocol for searching and selecting the scientific contributions to be analysed. Citation analysis is used to map a current research front and a bibliographic coupling is conducted to detect the connection networks in current literature.

Biodiversity accounting is articulated in five thematic clusters (sub-areas), such as “Natural resource management”, “Biodiversity economic evaluation”, “Natural capital accounting”, “Biodiversity accountability” and “Biodiversity disclosure and reporting”. Critical insights emerge from the content analysis of these sub-areas.

The analysis of the thematic evolution of the biodiversity accounting literature provides useful insights to inform both practice and research and infer implications for managers, policymakers and scholars by outlining three main areas of intervention, i.e. adjusting evaluation tools, integrating ecological knowledge and establishing corporate social legitimacy.

Currently, the level of biodiversity reporting is pitifully low. Therefore, organisations should properly manage biodiversity by integrating diverse and sometimes competing forms of knowledge for the stable and resilient flow of ecosystem services for future generations.

This paper not only updates and enriches the current state of the art but also identifies five thematic areas of the biodiversity accounting literature for theoretical and practical considerations.

This paper aims to review the existing literature systematically so as to contribute towards a better understanding of methodological problems of the classical statistical techniques, artificially intelligent expert systems and theoretical approaches to solve the corporate failure syndrome.

This paper presented a systematic review of 83 articles reporting 137 prediction failure models published within 1966-2012 in scholarly reviewed journals in four main disciplines, namely, accounting, finance, banking and economics. The authors performed the systematic literature review with five main sources, namely, Science Direct, Google Scholar, Wiley Interscience, Metalib, Web of Science and Business Source Complete of the Social Sciences. The review modified the approaches used by Aziz and Dar (2006), Ravi and Ravi (2007) and Balcaen and Ooghe (2006).

The results indicate significant body of prior literature on prediction of corporate failure, but a theoretically sound, highly accurate, simple and widely used corporate failure prediction model for stakeholders has yet to be developed.

This paper contributes towards a systematic understanding of the methodological problems associated with the statistical, artificially intelligent expert systems and theoretical approaches to solve the corporate failure prediction problems faced by firms in 11 countries.

A gap between research and practice is commonly perceived throughout accounting academia. However, empirical evidence on the magnitude of this detachment remains scarce. The authors provide new evidence to the ongoing debate by introducing a novel topic-based approach to capture the research-practice gap and quantify its extent. They also explore regional differences in the research-practice gap.

The authors apply the unsupervised machine learning approach Latent Dirichlet allocation (LDA) to compare the topical composition of 2,251 articles from six premier research, practice and bridging journals from the USA and Europe between 2009 and 2019. The authors extend the existing methods of summarizing literature and develop metrics that allow researchers to evaluate the research-practice gap. The authors conduct a plethora of additional analyses to corroborate the findings.

The results substantiate a pronounced topic-related research-practice gap in accounting literature and document its statistical significance. Moreover, the authors uncover that this gap is more pronounced in the USA than in Europe, highlighting the importance of institutional differences between academic communities.

The authors objectify the debate about the extent of a research-practice gap and stimulate further discussions about explanations and consequences.

To the best of the authors' knowledge, this is the first paper to deploy a rigorous machine learning approach to measure a topic-based research-practice gap in the accounting literature. Additionally, the authors provide theoretical rationales for the extent and regional differences in the research-practice gap.

This paper aims to present a systematic literature review and bibliometric analysis of research papers published on chat generative pre-trained transformer (ChatGPT), an OpenAI-developed large-scale generative language model. The study’s objective is to provide a comprehensive assessment of the present status of research on ChatGPT and identify current trends and themes in the literature.

A total of 328 research article data was extracted from Scopus for bibliometric analysis, to investigate publishing trends, productive countries and keyword analysis around the topic and 34 relevant research publications were selected for an in-depth systematic literature review.

The findings indicate that ChatGPT research is still in its early stages, with the current emphasis on applications such as natural language processing and understanding, dialogue systems, speech processing and recognition, learning systems, chatbots and response generation. The USA is at the forefront of publishing on this topic and new keywords, e.g. “patient care”, “medical”, “higher education” and so on are emerging themes around the topic.

These findings underscore the importance of ongoing research and development to address these limitations and ensure that ChatGPT is used responsibly and ethically. While systematic review research on ChatGPT heralds exciting opportunities, it also demands a careful understanding of its nuances to harness its potential effectively.

Overall, this study provides a valuable resource for researchers and practitioners interested in ChatGPT at this early stage and helps to identify the grey areas around this topic.

The purpose of this study is to identify, recap and evaluate the state-of-the-art linkage between International Financial Reporting Standards (IFRS) and earnings management (EM).

A bibliometric analysis of 249 publications from the Web of Science (WoS) database was carried out, employing both the techniques of performance analysis and science mapping and the Bibliometrix R and VOSviewer tools.

The results of the performance analysis suggest that the publication and citation trends of the interplay of the IFRS and EM fields show an upward trend over time that most of the influential institutions emanate from the US and a significant percentage of articles published in this field emanate from high-quality journals. Science mapping via co-authorship analysis elucidates that more collaborative efforts among authors are needed in the future in this field. Bibliographic coupling analysis bifurcates the studies into six clusters and reveals the major themes and their evolution. Co-word analysis unfolds emerging trends that could be further explored, thus becoming possible future research avenues.

To the best of the authors' knowledge, no other study has attempted a bibliometric analysis of research on the relationship between IFRS and EM. This article fills this research gap and makes its contribution to the scientific community by presenting recent developments in this body of knowledge and suggesting future research avenues.

This article develops a review of the university social responsibility literature to identify the most influential countries, authors, journals, and institutions, their structure, and research lines.

The review was carried out from a bibliometric and network analysis of documents published in the Web of Science database.

In total, 192 documents were found that were scientifically mapped in this field. From the network analysis, four research perspectives were identified: strategic impact management policy, user and its stakeholders, service-learning and its contribution to user, and theories, approaches, and strategies of University Social Responsibility (USR). Finally, the agenda for future research are presented.

The present work carries out a bibliometric and network analysis that seeks to contribute to the literature on USR, identifying its current perspectives and future lines of research.

The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry.

The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature.

A significant rise in construction big data research is identified with an increasing trend in number of yearly articles. The main themes discussed were big data as a concept, big data analytical methods/techniques, big data opportunities – challenges and big data application. The paper emphasises “the implication of big data in to overall sustainability” as a gap that needs to be addressed. These implications are categorised as social, economic and environmental aspects.

The SLR is carried out for construction technology and management research for the time period of 2007–2017 in Scopus and emerald databases only.

The paper enables practitioners to explore the key themes discussed around big data research as well as the practical applicability of big data techniques. The advances in existing big data research inform practitioners the current social, economic and environmental implications of big data which would ultimately help them to incorporate into their strategies to pursue competitive advantage. Identification of knowledge gaps helps keep the academic research move forward for a continuously evolving body of knowledge. The suggested new research avenues will inform future researchers for potential trending and untouched areas for research.

Identification of knowledge gaps helps keep the academic research move forward for continuous improvement while learning. The continuously evolving body of knowledge is an asset to the society in terms of revealing the truth about emerging technologies.

There is currently no comprehensive review that addresses social, economic and environmental implications of big data in construction literature. Through this paper, these gaps are identified and filled in an understandable way. This paper establishes these gaps as key issues to consider for the continuous future improvement of big data research in the context of the construction industry.

The popularity of massive open online courses (MOOCs) has attracted worldwide research interest. This study aims to identify and summarize the research foci (e.g. themes, methods, contexts, etc.) and discuss the new directions and trends of MOOC research in the context of Mainland China.

A systematic review of the published MOOC research papers in Mainland China was conducted with the following inclusion criteria: (1) papers written in English; (2) context focused on Mainland China; and (3) empirical studies. Three main issues were explored with the selected 70 papers: (1) research methods (data collection and analysis); (2) the research foci; and (3) research objects.

The results found that the major MOOC research in China was quantitative, mostly using one method to collect data. Most studies collected data through the databases of MOOC platforms and survey techniques, which was consistent with the widely used descriptive statistics for data analysis. Learner-focused themes were investigated the most, aligning with the result that learners were the most popular research objects.

The findings suggest that using new technology tools, such as the Big Data approach for learning analytics, may transform traditional MOOC research into new practices. Transdisciplinary research concepts may also provide an alternative evolving model for constructing collaboratively dynamic research frameworks under the changing technologies and paradigms. Meanwhile, educational research traditions, such as qualitative methods, contribute to scaffolding MOOC research for more pragmatic applications.

Most systematic reviews on MOOCs focus on general or regional contexts other than Mainland China, and scant MOOC review is based on published English papers about Mainland China.