The COVID-19 pandemic, while overwhelmingly negative in its impact on society, did highlight certain shifts, particularly in how we engage with digital environments (Shen et al., 2022). The accelerated digitisation of work and life processes stands out as one of the most pronounced changes, impacting both scholars and students (Fernández et al., 2023; Trevisan et al., 2024), although often with mixed results regarding mental and emotional wellbeing (Newson et al., 2024). Research into human information behaviour has shed light on how these pandemic-induced shifts alter the ways individuals seek and process information, as the need for reliable information becomes critical during crises (Montesi, 2021). The increase in the health information seeking reflects this, especially as it pertains to adjustments in daily life and physical health concerns during the pandemic (Kelly & Sharot, 2021; Ni et al., 2024). However, while seeking health information naturally aligns with pandemic-driven needs related to everyday life or physical condition (Kelly & Sharot, 2021), it is not only health -related information that has changed focus. The growing reliance on digital platforms for academic communication and scholarly discourse has also become a prominent post-pandemic theme, with deeper implications for how we approach the dissemination and sharing of knowledge. The literature points to a significant increase in the importance of scholarly communication practices, particularly those that intersect with open access, open science, and other models of academic openness (Frank et al., 2023; Rowley, 2023; Seguya et al., 2023). The processes of information seeking is evolving with the academic community as issues around predatory publishing (Downes, 2023; Oermann et al., 2023), open science (Chakravorty et al., 2022; Okafor et al., 2022; Ross-Hellauer et al., 2022), open data initiatives (Prince, 2023), and the use of open educational resources (Sousa et al., 2023; Tlili et al., 2023) gain prominence.. Switching to open communication models is closely tied to the use of digital resources, the influences driving academic productivity, and broader transformations in higher education, management, and library and information science (Fitzgerald, 2020; Ma, 2023). This expanding focus on digital scholarly communication promises to shape the future of information seeking research, especially in the light of ongoing digital transformation across various academic disciplines.

However, it remains uncertain whether the COVID-19 pandemic and its restrictions had a causal effect on the observed rise of information seeking and scholarly communication as research topics, especially in terms of how the digital shifts arising from lockdowns might have influenced impact metrics. This paper seeks to provide empirical evidence supporting the idea that these research topics gained greater scientific prominence across various fields in the post-pandemic era. To address this uncertainty, it is essential to establish the causal role of the pandemic in amplifying the impact of research on these topics. This study proposes to illustrate this through a bibliometric analysis, employing a novel causal inference framework with the C-ARIMA model (Menchetti et al., 2023) to establish genuine causation, rather than association, based on bibliometric data drawn from the Scopus database.

The COVID-19 pandemic reshaped academic research, creating a complex landscape for academic work. As university buildings closed and researchers transitioned to remote work, a dichotomy emerged in academic publishing. Lee and Haupt (2021) documented a global influx of COVID-19 publications, reflecting the rapid mobilisation of the scientific community. However, Püttmann and Thomsen (2024) revealed a simultaneous decline in non-COVID-related research output. However, this was a global trend that did not capture fluctuations in a specific field or research topic.

The review by Huvila and Gorichanaz (2024) partially supports the assumption of this paper: Information seeking has a strong position in information science studies after pandemics. Searching for health information leads to the importance of the topic. The short-and long-term effects of the pandemic on information seeking were analysed, indicating that information seeking should present a positive trend in field-specific importance (Huvila & Gorichanaz, 2024). Wilson (2018) presented a similar assumption regarding the diffusion of information seeking behaviour research beyond library and information science just a few years before the global crisis related to the pandemic.

The impact of the pandemic on academic productivity varied. Some researchers found unexpected writing time during lockdowns, while others struggled with remote work constraints. Jackman et al. (2022) found that some UK doctoral students and early career researchers reported increased productivity and improved work-life balance. However, widespread disruptions to research activities were observed, particularly in laboratory-dependent disciplines, causing long term problems related to fewer submissions and low quality published articles (Yang & Li, 2024). While we can presume that some topics related to information seeking might emerge and gain importance, there is a separate trend to such research output related to temporal decrease of academic productivity, which varies field-to-field.

Lowering the quantity of output does not necessarily indicate a decrease in the impact of research or the direction of further research, its pure quality, or a lesser importance of publications in some field or topics. Myers et al. (2020) found that scientists in experimental disciplines experienced severe declines in research time, with reductions of 30-40% below pre-pandemic levels, but fields less dependent on physical infrastructure reported minimal disruptions, underscoring the pandemic’s uneven impact on academia. Cui, Ding and Zhu (2022) also showed fluctuations in preprints published in different fields. Education saw an increase in scholarly output during the pandemic, while fields such as anthropology, cognitive sciences, and information systems showed a decline.

Another transformation lies in the disruption of international research collaborations. Gao et al. (2021) observed a significant decrease in new co-authorships of non-COVID-19 papers in 2020, suggesting a contraction of global scientific networks. However, in early 2021, scientists exhibited fewer declines in number of collaborations compared to the pre-pandemic state and observed a smaller workload, indicating time-related combat to work-life normality (Gao et al., 2021).

Although Library and Information Science (LIS) students, researchers, and library professionals experienced some initial barriers related to remote communication (Begum & Habiba, 2023; de Wit & Altbach, 2023), there is no indication that the quality of research in field-specific topics, such as information seeking or scholarly communication, was significantly affected. Moreover, the evolving role of digital forms of scholarly communication, like the popularisation of preprints (Fraser et al., 2021), dealing with misinformation, is also closely aligned to predatory publishing (Otike et al., 2022), and the emerging importance of information seeking (Huvila & Gorichanaz, 2024), support the initial assumption that these processes has gained significant relevance as research topic and might gain impact across many fields of science.

While the pandemic’s immediate shocks in academia are documented, the aftermath that emerged during the subsequent period of stabilization remain under-examined. This study aims to verify whether pandemic-related restrictions and the digitization of academic work exerted a causal influence on the growing prominence of research in information seeking and scholarly communication. These topics are situated within the context of Library and Information Science (LIS), and their impact is measured by the cross-disciplinary and field-normalized citation metric. Classical statistical inference alone is insufficient for capturing the complexities of this problem, as it primarily addresses correlations without fully exploring counterfactual scenarios (Pearl, 2010). Instead, a framework grounded in causal inference is necessary, one that surpasses simple association-based reasoning and traditional experimental designs. To investigate how pandemic-induced restrictions shaped information seeking research, a causal inference approach is employed to transcend these limitations. Causal learning integrates methodologies from causal inference and machine learning, extending beyond correlation-based approaches by incorporating intervention-based modelling (Cheng et al., 2022). Unlike standard machine learning, which primarily seeks predictive accuracy, causal learning emphasizes estimating intervention effects and modelling distributional shifts using both statistical assumptions and data-driven learning.

Causal autoregressive integrated moving average (Causal ARIMA), as applied in this study, exemplifies this causal inference approach. While ARIMA is traditionally a statistical forecasting method, Causal ARIMA extends it by embedding intervention modelling within the potential outcomes framework, allowing for counterfactual estimation by projecting the pre-intervention trend forward to construct a synthetic non-intervened scenario (Menchetti et al., 2023; Yao et al., 2021). Unlike traditional regression-based methods, which assume fixed relationships between variables, causal learning in time series explicitly models dynamic changes in dependencies induced by interventions. This enables counterfactual estimation, such as assessing the absence of the pandemic, while isolating treatment effects and accounting for shifts in publication trends or disciplinary emphasis. As causal inference increasingly integrates representation learning and confounder adjustments, it strengthens time-series causal estimation, mitigating spurious correlations and reinforcing causal learning as a distinct methodological paradigm (Yao et al., 2021). Counterfactual estimation is critical in this context because it allows to differentiate the true effects of the pandemic on research dissemination from general publication trends, thereby isolating the causal impact of pandemic-related disruptions on scholarly communication.

This study uses a causal inference framework to model counterfactual scenarios and estimate the effects of interventions, following the principles of potential outcome theory, also known as the Rubin causal model (Imbens & Rubin, 2015). In this paradigm, our aim is to assess the impact of the COVID-19 pandemic on the outcome of research behaviour captured in bibliometric data related to our research topic, as defined by Scopus in the topic cluster: Information seeking; scholarly communication; adoption–TC.905. To do this, the observed post-pandemic data was compared with counterfactual data, using a forecasting ARIMA model to predict paper impact metrics in a scenario where the pandemic did not occur. This process allows us to evaluate the causation through predictive modelling and comparison of real world and counterfactual outcomes (Menchetti et al., 2023).

In line with the causal nature of most social science research questions (Pearl, 2010), this study represents a shift from traditional statistical inference to causal reasoning. These new methods aim to uncover the mechanisms behind observed phenomena and address "what if?" questions (Imai, Kim & Want, 2023), such as: ‘What would have happened if COVID-19 restrictions had not occurred?’ Pearl’s (2010) distinction between associational and causal concepts is key here: while associational concepts are derived from data distributions (e.g., the rise in information seeking publications post-2020), causality requires counterfactual thinking. For example, by comparing pre-pandemic data with machine learning models of bibliometric impact, we explore what impact trends might have been without the pandemic, an approach not possible thirty years ago (Pearl, 2000). Although other attempts have been made to bring causal inference methods into bibliometric research (for instance, Lars Wenaas (2021) used Bayesian structural time series to examine the readership effects of journals converting to open access, and, Riccaboni and Verginer (2022) employed a difference-in-differences model to detect shifts in life science research during the pandemic) the approach presented here advances that effort by focusing on field-normalized impact metrics and broader information seeking and scholarly communication topics.

The rapid digitisation of academic work and the urgent need for accessible research have reshaped the research landscape in ways that may persist beyond the immediate crisis. However, the present study does not directly confirm widespread changes to research practices or digital tool adoption. Rather, it focuses on whether the pandemic influenced the citation patterns of literature on information seeking and scholarly communication as a topic cluster by shifting research priorities toward digital transformations that address urgent concerns in these areas, thereby heightening scholarly attention. By examining how the pandemic caused the shifts in these research areas, the analysis supports the notion that the global crisis influenced scholarly attention to topics related to information seeking behaviours and scholarly communication, leading to unexpected increased field-specific impact (Riccaboni & Verginer, 2022).

Using the C-ARIMA model, key causal effects were identified, indicating the role of the pandemic in reshaping some research topics’ focus. This methodology goes beyond simple correlations, establishing a direct causal link between the pandemic and changes in research impact, an underutilised approach in scientometric studies (Li et al., 2023). By applying causal inference, the study bridges the theory-driven sociology of science with data-driven bibliometrics and scientometrics. Waltman (2016) emphasises that normalised citation impact indicators are critical in research evaluation because they account for disciplinary differences. This perspective is crucial for assessing the impact of the pandemic on research behaviour and academic output in multiple fields.

The observed increase in impact within related areas may reflect growing academic interest in how scholarly communication and information seeking evolved during the pandemic, rather than a direct measure of adoption of digital tools alone. The rise in research impact underscores the importance of these fields in navigating post-pandemic academic discourse, where digital reliance, misinformation, and the demand for open data have increased the value of research (Otike et al., 2022; Ross-Hellauer et al., 2022).

In practical terms, the application of causal inference in scientometrics offers valuable information for both academic institutions and policy makers. Identifying causal relationships between global crises, technological breakthroughs, and academic performance provides essential knowledge for institutional preparedness and policy adjustments. Future research could extend this approach to more granular studies of specific fields, journals, and collaborative practices, further exploring how crises shape knowledge production and dissemination across disciplines (Li et al., 2023).

Empirical studies could involve surveys and interviews with faculty to investigate the social processes driving the observed causal effects on information seeking and scholarly communication post-pandemic. While the pandemic likely triggered a digital transformation in research, communication, and academic collaboration, the present findings mainly suggest increased scholarly interest in these topics (e Rodrigues & Mandrekar, 2021; Elia, 2023). This trend aligns with the broader digitalisation of academia, where open access and efficient information retrieval as a research topic has increased. However, this change also raises ethical and policy concerns, such as data privacy, misinformation, and the commodification of scholarly output (Ross-Hellauer et al., 2022). Causal inference methods, which establish time-related causal links, are better suited to address these concerns than correlational studies, which can overlook the underlying drivers of these changes but still can impose some limitations (Cordero et al., 2018).

While this research applies a novel causal inference approach to bibliometric data, several limitations warrant attention. First, although the C-ARIMA model was chosen for its capacity to address time-diminishing trend effects in nonstationary time series, it was not systematically compared to other configurations, such as ARIMA or SARIMA variants, exponential smoothing, or other causal models like Bayesian Structural Time Series (Riccaboni & Verginer, 2022; Wenaas, 2021). Future methodological work could incorporate these alternative models to refine and validate the findings, given the complexities of bibliometric data. In this study, the pragmatic focus on achieving a reliable forecast with minimal errors remains crucial for effectively handling time-series challenges in bibliometric analysis.

Second, the study examines a single topic cluster (TC.905) from 2015–2022 occurring only in Scopus database, aggregated at a monthly level to manage temporal detail and statistical stability. Although this choice reduces noise from publication timing, other aggregation windows (e.g., quarterly) might reveal different patterns or capture disciplinary nuances, but would also reduce data points and necessitate a longer timeframe for analysis. Future investigations could explore specialized clusters to see whether similar trends surface in different publishing contexts.

Third, even though unimpacted covariates were considered, unobserved confounders remain possible (Pearl, 2000). Institutional policies, funding shifts, or other external influences may shape citation trends in ways not fully accounted for here. Incorporating Principal Component Analysis (PCA) to derive latent variables from multiple covariates has proven effective in ARIMA models (Hajirahimi et al., 2023). Hence, while these findings suggest a causal link between the pandemic and shifts in research impact, they should be interpreted with caution considering these constraints.

The adoption of causal inference methodologies in bibliometric analysis marks a significant leap forward in understanding the dynamics of scientific research and knowledge dissemination. These techniques allow for moving beyond descriptive analyses to make causal claims about the factors driving changes in research focus and impact (Li et al., 2023). This shift aligns with the growing recognition of the importance of causal reasoning in data science and machine learning (Pearl & Mackenzie, 2018). Causal reasoning supports more informed decision-making. By understanding the reasons behind observed changes in research practices, rather than simply observing them, academia can develop more effective and ethically sound strategies to address emerging trends. Based on the growing significance of information seeking research in post-pandemic scholarly communication, this hypothesis is proposed for future studies in both bibliometric and empirical contexts. The increase in research impact related to information seeking behaviour and scholarly communication post-pandemic is driven by an expanding reliance on digital and open science models for knowledge dissemination. This shift, further amplified by the increasing demand for accessible information during global crises, has elevated the prominence and influence of research in these fields. This hypothesis promotes a deeper exploration into how digital transformations and the rise of open science will continue shaping research practices, collaborative networks, and the overall impact of academic work in a post-pandemic world.

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