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This study synthesizes the role of artificial intelligence (AI) and automation in systematic literature reviews (SLRs), focusing in particular on efficiency, methodological quality and human–machine collaboration.

A systematic review methodology was applied, analyzing studies from Scopus and Web of Science databases to explore the use of AI and automation in SLRs. A final sample of 28 articles was selected through a rigorous and interdisciplinary screening process.

Our analysis leads to seven themes: human and machine collaboration; efficiency and time savings with AI; methodological quality; analytical methods used in SLRs; analytical tools used in SLRs; SLR stages AI is utilized for and living systematic reviews. These themes highlight AI’s role in enhancing SLR efficiency and quality while emphasizing the critical role of human oversight.

The rapid advancement of AI technologies presents a challenge in capturing the current state of research, suggesting the need for ongoing evaluation and theory development on human–machine collaboration.

The findings suggest the importance of continuously updating AI applications for SLRs and advocating for living systematic reviews to ensure relevance and utility in fast-evolving fields.

Integrating AI and automation in SLRs could democratize access to up-to-date research syntheses, informing policy and practice across various disciplines and redefining the researcher’s role in the digital age.

This review offers a unique synthesis of AI and automation contributions to SLRs, proposing a conceptual model emphasizing the synergy between human expertise and machine efficiency to improve methodological quality.

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.

The aim of this paper is to assess whether the current European target to increase the areas under organic farming to 25% by 2030 is attainable and whether the simple increase in areas under organic farming may be sufficient to improve the sustainability of European agriculture.

The analysis has been carried out through a simple data processing related to areas under organic farming, for the period 2012–2020 (Eurostat database), in order to highlight the trends of areas under organic farming and to verify whether the annual average change rates may be compatible with the stated target.

The analysis showed that organic farming has a productive weight not corresponding to the amount on the total of the areas under cultivation and a small impact on the total of food consumption. It is a plausible hypothesis, the one that shows the increase in areas under organic farming will engage forms of agriculture and farms that, already, are more sustainable, so the achievement of 25% target will not particularly impact the European potential productive and the less environmental sustainable forms of agriculture.

This paper contributes to the debate, involving scientific community, policy maker and civil society, about the real contribution of organic farming to sustainability, and it will be developed in future research.