Accelerating research processes with Scopus AI: A place branding case study

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Academic databases play a crucial role in advancing science by hosting a vast array of peer-reviewed literature. However, academic database search tools involve a relatively slow and rather unintuitive process of searching and evaluating content.

To address these challenges, in January 2024, Elsevier introduced Scopus AI, a generative artificial intelligence that  synthesises evidence indexed in Scopus based on prompts. This study assesses the utility of Scopus AI (in its beta version at the time of the research), within the context of a doctoral thesis through a specific case study.

By employing a relational prompt and three follow-up questions, the study aims to pinpoint intersections between different topics within the realm of Social Sciences and, more specifically, Communication, with a case on place branding.

The consolidated result provides an initial list of references, offers a comprehensive overview, and allows to generate a meta-synthesis based on the summaries provided by each prompt. Scopus AI (beta) presents features that enable researchers to identify influential authors and works, explore relevant keywords, review recent literature, and identify potential research gaps.

Although Scopus AI has some limitations, such as the dependence on the abstracts of documents indexed in Scopus, the simplification of concepts, or the relative disconnection between arguments, the results demonstrate the value of this tool in accelerating research processes, as it synthesises research in a given area, maps its main characteristics and allows for information discovery.

Keywords Scopus AI beta; Artificial intelligence; Academic databases; Academic research; Doctoral theses.


In recent years, the expansion of artificial intelligence (AI) technologies has been exponential, reaching many sectors of society. This surge in both scale and scope of AI development and implementation is unprecedented (Hajkowicz et al., 2023). Alongside other innovative technologies, AI serves as a catalyst for what has been coined the fourth revolution (Ivaldi et al., 2021) and the fourth wave of mediatisation, rooted in big data (Couldry & Hepp, 2017). The scientific and research sector is one of the early adopters of artificial intelligence and has grown in virtually all disciplines (Hajkowicz et al., 2023).

Although some AI tools have emerged in the last few years, they embody significant disruptive potential within academia and scholarly publishing (Kaebnick et al., 2023; Lopezosa & Codina, 2023; Lund et al., 2023), by reconfiguring the dynamics of scientific discovery and influencing the organisation of science (Bianchini et al., 2022). However, the implementation of AI technologies also poses several challenges, including ethical, political, legal and policy concerns, organisational, managerial and technological obstacles, data-related issues and social implications and economic impacts (Dwivedi et al., 2021).

Searching and navigating scholarly literature

The basis of scientific advancement is to make an original contribution to a field of research (Baptista et al., 2015). Yet, the research environment can be daunting, since it contains a vast amount of published research in many academic outlets indexed in several databases. This context can be particularly challenging for early-career researchers, given that scientific advancement is a cumulative process (Bird, 2007; Codina, 2020).

In addition, any new research project should include a review of previous literature, as such reviews are integral components of all research efforts. Literature reviews synthesise previous research. Their evidence-based approach is crucial for understanding research fronts, advancing theory development, and identifying interdisciplinary areas and research gaps, or areas that require further research (Snyder, 2019).

What is more, literature reviews are part of the ethical requirements of research, as outlined by the European Code of Conduct for Research Integrity (ALLEA, 2023, p. 7). However, one of the main challenges when conducting research remains navigating a sheer quantity of literature, as well as searching, screening and extracting data manually (De la Torre‐López et al., 2023).AI tools are starting to transform conventional research practices. In this context, literature reviews are of particular interest, since they handle vast amounts of partially organised information, and are an integral part of research across all disciplines (Wagner et al., 2022).

Furthermore, manual methods when approaching systematic reviews can prove time consuming, expensive and impractical. As a result, researchers use machine learning to streamline the process of conducting evidence syntheses (Marshall & Wallace, 2019). This constitutes an avenue for further research, as AI tools can assist in the process and hold the potential to transform the way research is conducted (Burger et al., 2023).

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The role of AI in research processes

In recent years, academic search tools incorporating AI technology have proliferated. This is the case of the Dimensions database or the Semantic Scholar search engine, as well as tools such as Elicit and Scite.

These platforms provide a wide range of academic literature and contribute to accelerating search and discovery of academic information by facilitating the understanding of scientific texts and the extraction of relevant data, as well as the evaluation of citations.

The abundance of information and the rise of AI that characterise the 21st century, require students and researchers to be both data and AI literate: to search, interpret and manage data effectively, as well as understand how AI tools can streamline their work (UNESCO, 2019). Yet, while automated approaches offer the advantage of accelerating research processes, few schools and universities “have developed institutional policies and/or formal guidelines on the use of generative AI applications” (UNESCO, 2023).

There is a misalignment between skills adopted in the academic setting, and those required to thrive in the evolving world of AI technologies (Dwivedi et al., 2021). Besides, the adoption of AI technologies is also limited by the learning curve involved and the lack of studies evaluating their benefits (De La Torre‐López et al., 2023).


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