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LIBRES: Library and Information Science Research E-Journal

Publication analysis, about the publication.

Title:   LIBRES: Library and Information Science Research e-journal

ISSN : 1058-6768

Website:   https://www.libres-ejournal.info/

Purpose, objective, or mission:   LIBRES , an international refereed e-journal, publishes research and scholarly articles in library and information science and services (LIS). “It has a particular focus on research in emerging areas of LIS, synthesis of LIS research areas, and on novel perspectives and conceptions that advance theory and practice.” 1

Target audience:   LIBRES  is for information science professionals and librarians interested in all aspects of LIS research and scholarship, but especially in emerging areas, novel perspectives, and new understandings of LIS theory and practice. 2

Publisher:   LIBRES  is jointly published by the Wee Kim Wee School of Communication & Information and the NTU Libraries at Nanyang Technological University in Singapore. It was previously published by the Department of Information Studies at Curtin University in Perth, Western Australia. 3

Peer reviewed?  At least two referees blind review each paper. 4

Type:   LIS scholarly journal.

Medium:   LIBRES  is an online, open-access journal.

Content:  This journal has three main sections, Research Papers, Synthesis & Perspectives, and Special Sections. The journal publishes research papers on studies that advance LIS, synthesis papers that survey areas of LIS for new or better understandings, and scholarly opinion or perspective papers that explore new conceptions of LIS. 5  Each Special Section is devoted to papers from conferences from around the globe, promoting the journal’s commitment to regional LIS scholarship. 6

Frequency of publication:  Twice a year, in June and December. 7

About the publication’s submission guidelines

Location of submission guidelines:   Author Guidelines .

Types of contributions accepted:   LIBRES  accepts scholarly research, synthesis, and perspective papers on any aspect of LIS, especially in emerging areas or with novel conceptions that advance theory and practice.  8

Submission and review process:  Submissions should be sent in Microsoft Word documents to the editor at [email protected]. Submissions are usually reviewed within 60 days of receipt. Papers should not be under review or published elsewhere. “The reviews will pay particular attention to whether the papers are interesting, useful, thoughtful, and a significant contribution to knowledge in the LIS field.” 9

Editorial tone:  The journal uses a formal academic style. The journal’s official language is English, but the editor encourages submissions from developing countries and countries where English is not the native language; revision and editing for readability are part of the publication process. 10

Style guide used:   Publication Manual of the American Psychological Association , 6th edition. 11

Conclusion: Evaluation of publication’s potential for LIS authors

LIBRES  is focused on new research and novel perspectives from the LIS international academic community. Authors can submit to either the Research Papers section or to the Synthesis & Perspectives section. The journal’s authorship is international, and it publishes articles from developed and developing countries;  LIBRES  takes “a nurturing attitude towards papers and authors,” and the editorial board provides “substantive guidance to the authors,” especially those who are not native English speakers. 12  “In subject coverage, it has a particular strength in library/information service,” and it promotes worldwide regional LIS community scholarship by publishing conference papers. 13  It publishes high-quality research, often on technology and service, from a many different countries, pushing LIS regional and international innovation forward.

Audience analysis

About the publication’s readers.

Publication circulation:  Data not available.

Audience location and language or cultural considerations:    LIBRES  is published in English and is international in scope, 14  and the editorial board is especially interested in linking up with “regional LIS research communities worldwide.” 15

Reader characteristics:  The audience of  LIBRES  is LIS academics and professionals from around the world, 16  and papers are published by authors from the United States, Mexico, Cuba, Qatar, India, Pakistan, Saudi Arabia, and Malaysia, to name a few. The conference papers in the Special Sections expand its international scope in terms of research and readership.

Knowledge of LIS subject matter:  Readers will have a professional and scholarly understanding of LIS practice and research.

Conclusion: Analysis of reader characteristics and their potential impact on authors

This scholarly journal’s readers will expect formal research and high-level syntheses. Topics for submission include current and emerging LIS research areas, emerging technology, and library service. For LIS professional and student researchers,  LIBRES  is a good place to research that investigates practices within library and information science environments and advances in new and emerging technology. For LIS scholars,  LIBRES  encourages synthesis papers that consider theory and practice in a new light and opinion and perspective pieces that explore new ideas in LIS.

Last updated:  January 30, 2018

• “About LIBRES,”  LIBRES,  accessed January 26, 2018,  https://www.libres-ejournal.info/about-libres/ .
• “About LIBRES.”
• “Author Guidelines,”  LIBRES,  accessed January 26, 2018,  https://www.libres-ejournal.info/author-guidelines/ .
• For example, Special Section: Digital Curation Projects and Research in Asia,  LIBRIS  26, no. 1 (2018), accessed January 26, 2018,  https://www.libres-ejournal.info/all-issues/volume-26-issue-1/ .
• “Author Guidelines.”
• Chris Khoo, “Editorial,”  LIBRIS  25, no. 1 (2015), accessed January 31, 2018,  https://www.libres-ejournal.info/1621/ .
• Chris Khoo, “Editorial,”  LIBRIS  24, no. 1 (2014), accessed January 31, 2018,  https://www.libres-ejournal.info/1369/ .
• Khoo, “Editorial” (2014).
• Khoo, “Editorial”  ( 2014).

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Theses/Dissertations from 2017 2017

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Alternate Academy: Investigating the Use of Open Educational Resources by Students at the University of Lagos in Nigeria , Daniel Onaifo

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Contentious information: Accounts of knowledge production, circulation and consumption in transitional Egypt , Ahmad Kamal

Multilingual Information Access: Practices and Perceptions of Bi/multilingual Academic Users , Peggy I. Nzomo

Words to Live By: How Experience Shapes our Information World at Work, Play and in Everyday Life , Angela Pollak

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©1878 - 2016 Western University

LIS research across 50 years: content analysis of journal articles

Journal of Documentation

ISSN : 0022-0418

Article publication date: 15 July 2021

Issue publication date: 19 December 2022

This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015.

Design/methodology/approach

The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology.

The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed.

Originality/value

Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

• Content analysis
• Statistical analysis
• Information science
• Library and information science
• Longitudinal study
• Scholarly journal articles

Järvelin, K. and Vakkari, P. (2022), "LIS research across 50 years: content analysis of journal articles", Journal of Documentation , Vol. 78 No. 7, pp. 65-88. https://doi.org/10.1108/JD-03-2021-0062

Emerald Publishing Limited

Copyright © 2021, Kalervo Järvelin and Pertti Vakkari

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Several studies indicate that (Library and) Information Science (LIS) is under reorientation both topically and methodologically ( Hsiao and Chen, 2020 ; Ma and Lund, 2020 ). Pressures toward this are due to developments in underlying technologies of dissemination of information and in the corresponding research areas like digital libraries, data mining, machine learning and web science, among others. It is timely to investigate how these pressures materialize in the published LIS research.

To understand the state of a discipline, and consciously build its possible futures, it is necessary to know how the characteristics of its research have developed to date. This requires analysis of the development of research foci and methodological choices. Understanding the current state also helps planning the curricula and research. For the former one learns what courses are necessary, which strategically desirable, which minors are most useful and from where to recruit students and teachers. For research planning, one gains a mapping of the active research areas and hints for building successful alliances.

When building such understanding, past literature has analyzed research topics and methods in LIS (e.g. Åström, 2007 ; Tuomaala et al. , 2014 ). The present paper reports a content analysis of articles published in top scholarly journals of LIS, following the study design by Tuomaala et al. (2014) and covering research from 1965 to 2015 at 20 years' intervals.

The paper has two research questions (RQs): (1) What was the topical and methodological state of LIS research in 2015? We analyze this through articles published in core LIS journals in 2015. (2) How has LIS evolved from 1965 to 2015? We examine this in the light of comparable statistics for the years 1965, 1985 and 2005 and 2015, reporting on the rise and fall of LIS research foci and methodological choices.

Our dataset comes from scholarly English language LIS journals. Journal articles have been almost the sole data source in recent studies of LIS research publications (e.g. Milojevic et al. , 2011 ; Figuerola et al. , 2017 ; Han, 2020 ). We analyze the trends in LIS research over 50 years. The period covered is notably longer than in earlier studies, which typically cover about 20 years (e.g. Hou et al. , 2018 ; Han, 2020 ), at most 36 years ( Figuerola et al. , 2017 ). To enhance comparability of findings with Tuomaala et al. (2014) we tried to keep the set of LIS journals stable over the years.

By following Tuomaala et al. (2014) we also adopted their criteria in journal selection. Their theoretically oriented criteria meant purposive selection of core journals matching the characterization of LIS as the provision of access to desired information ( Vakkari, 1994 ). We have therefore avoided inclusion of journals belonging to other disciplines, like management information systems, as suggested by Abritzah et al. (2015) and Huang et al. (2019) . Their practical criteria meant focusing on scholarly LIS journals with wide distribution, international editorial board and publication policy – which had been assessed as core journals by other researchers (e.g. by Peritz, 1980 ; Tuomaala et al. , 2014 ). We arrived at a set of 30+ journals for collecting an article set of 1,514 articles.

To construct the dataset for analysis, we performed a quantitative intellectual content analysis, classifying articles along eight dimensions covering their topical content and methodology. Our LIS classification is based on Tuomaala et al. (2014) and provides a tool for organizing and interpreting findings without an atheoretical selection and naming of topical clusters to represent LIS quite typical in scientometric analyses. In addition to the topical developments, we report on methodological developments of LIS during 50 years. Intellectual content analysis is appropriate in separating topical and methodological aspects. For example, it becomes natural to classify an article that topically belongs to scientometrics but employs a mathematical research strategy (non-empirical), and another topically in scientometrics but employs citation analysis (an empirical strategy).

2. Literature review

There are no recent major conceptual analyses of LIS like in earlier years (c.f. Tuomaala et al. , 2014 ), while there are empirical ones. Most of these studies have applied scientometric research strategies, and only a few have used content analysis. Next, we will present studies which have analyzed LIS research after 2014.

2.1 Research topics

Ma and Lund (2020) is the only study using content analysis to explore the evolution of LIS. They analyzed the topics and methods of scholarly articles in 31 major LIS journals in 2006, 2012 and 2018, using categorizations of Tuomaala et al. (2014) . The findings indicated a shift of emphasis toward scholarly communication and information seeking topics, along with a reduction of the share of information storage and retrieval (IS&R) and L&I service topics. In 2018 IS&R was the most common research topic followed by scholarly communication.

Chang et al. (2015) analyzed the evolution of LIS based on keywords, bibliographical coupling and co-citation analyses during four five-year periods between 1995 and 2014. Data consisted of research articles in 10 major LIS journals. The cited top-5% journal articles were analyzed. The authors reduced LIS intellectually from various numbers of clusters into four major fields. Information seeking and retrieval took the largest share of articles followed by bibliometrics in 1995–1999. In the later periods, the popularity of bibliometrics increased its share beyond 75% during 2005–2014.

Liu et al. (2015) detected the topical structure of LIS in 2001–2013 by formal concept analysis. Data consisted of first authors and keywords of articles in 16 prominent LIS journals. Authors were chosen based on their productivity and impact. From the articles of the 60 key authors, 99 keywords with highest frequency were selected for the analysis. Nine main topics were identified: bibliometrics, scientometrics and informetrics, citation analysis, IR, information behavior, libraries, user studies, social network analysis, information visualization and webometrics.

Onyancha (2016) explored the evolution of LIS using author-supplied keywords in research articles between 1971 and 2015. Data were collected from Web of Science in the research area “Information Science Library Science”. The results hint that the focus of LIS has changed from information system design and management in the 1970s to scientific communication, information retrieval, information management and user education by 2015. The data consist of a mix of articles representing management information systems and LIS due to the classification of research fields in WoS. In addition, only 26% of the articles included author-supplied keywords reducing the representativeness of the data. Author-supplied keywords also run the risk of being inconsistent due to varying (or missing) keyword authority lists; low interest and motivation, and vague indexing skills on behalf of the authors. One may therefore ask to what extent the findings reliably reflect the evolution of LIS.

Figuerola et al. (2017) mapped the evolution of LIS in 1978–2014 by topic modeling. Their data included titles and abstracts of peer-reviewed publications in Lisa covering 92,705 items from 737 journals. Modeling produced 19 topics, which were grouped by human experts into four broader areas: process, information technology, libraries and specific areas of application. During the years topics associated with libraries lost in their share, while topics concerning searching and evaluation of search systems, and specific areas of application like digital humanities or informetrics, in particular, have gained weight.

Hou et al. (2018) analyzed the structure information science between 1996 and 2016 applying document co-citation analysis. They selected ten representative journals for the years 2009–2016 based on the journal co-citation analysis of JASIST, Scientometrics and Journal of Informetrics, while 12 core journals from an earlier study represented the earlier years. The results show that the core topics from 1996 to 2008 were IR, webometrics and citation behavior, while in the latter period scientometric indicators, citation analysis, scientific collaboration and information behavior, and particularly science evaluation indicators formed the core. Thus, the core topics in information science have developed towards scientific and professional communication (S&PC). The results are biased towards scientometrics, because two-thirds of journals on which journal selection was based, represented scientometrics.

Li et al. (2019) surveyed LIS trends between 1989 and 2018 by document co-citation analysis. Data consisted of documents retrieved from Web of Science by the theme field “information science and library science”. The output included 88,304 publications from 159 journals and proceedings. The study produced eight clusters: IR, social media, information systems, information behavior, bibliometrics and webometrics, science evaluation and knowledge management. IR, information systems and bibliometrics and webometrics are the oldest topics, while social media and science evaluation are the most recent ones.

Han (2020) investigated LIS evolution by analyzing journal articles using Latent Dirichlet Allocation. The explored period was between 1996 and 2019, which were divided into five sub-periods. For each period, 10 highest scoring LIS journals in journal ranking (JCR) were selected for analysis which focused on title, abstract and keywords in each article ( n  = 14,053). Ten clusters in each period were chosen for final analysis.

The results show that library service-related topics disappear after 1996–2005, while topics in IR proliferate during 2000–2010. Various topics in citation analysis and bibliometrics are well represented during the years. The topics belonging to information and knowledge management increase after 2005. The author divided the 10 topics into three larger fields in LIS: library science, bibliometrics and information science and other related issues. Library science disappeared after 2005, bibliometrics was a well-represented field until the last period, while information science and related issues were a strong field across the periods.

The results are biased in two ways. First, the highest-ranking journals varied greatly between the periods producing highly varied clusters. It is an open question how representative these topical changes are in LIS. Second, it has been shown that LIS journals in JCR represent two different fields: management information systems and LIS ( Abritzah et al. , 2015 ; Huang et al. , 2019 ). The topical structures of these fields differ notably. Thus, the produced topical map of LIS contains topics from other fields.

Hsiao and Chen (2020) investigated LIS subfields between 2009 and 2018 by word bibliographic coupling. They selected 44 journals from JCR in the category “Information science, library science”. They included only LIS journals, excluding journals belonging to management information systems. The data consisted of abstracts and author keywords in 21,066 articles. The observation period was divided into two: 2009–2013 and 2014–2018. The top-3 subtopics among 25 subtopics in both periods were scientific impact and research quality, information behavior and users and technology adoption. By grouping the subtopics, the study identified six main topics, including scholarly communication and scientometrics, information behavior and IR, applications of technology, library services and management, health information and technology and computer science techniques.

Miyata et al. (2020) applied Latent Dirichlet Allocation to identify LIS topics. They analyzed full texts of articles in five LIS journals in 2000–2002 and 2015–2017. Thirty topics in each period were labeled. The first period topics were grouped into six fields: IR (10 subtopics), information search and user (10), library (4), scholarly communication (4), library and information science (1) and bibliometrics (1). In 2015–2017, topics were grouped into five fields: IR (2 subtopics), information search and user (16), library (1), scholarly communication (6) and tweet analysis (5). In the first period, IR and information search (IS) included most topics, while in the second, the number of topics in IR decreased and in IS increased notably. In the field library, the number of topics decreased during the periods observed, while that of scholarly communication increased.

2.2 Research methods

Ma and Lund (2020) found that in LIS, experiment was the most popular method in 2006, 2012 and 2018 with a share of about 30%. The second in popularity was survey. Its share increased from about 19% to 25%. Citation analysis was the third with about a 13–14% share. The shares of the top research methods of the major topics of IR, information seeking, scholarly communication and L&I services remained relatively stable across the data points. Experiment was the most common method both in IR and information seeking, while citation analysis dominated in scholarly communication, and survey in L&I services.

Chu (2015) identified research methods in LIS by analyzing research articles in Journal of Documentation, JASIST and LISR in 2001–2010. The four most common methods in all journals were theoretical approach, content analysis, questionnaire and experiment.

Ullah and Ameen (2018) provided a meta-analysis of methods applied in LIS based on 58 source publications published in 1980–2016. They created unifying categorizations of variables by re-coding method variables in the source publications. They found that empirical, descriptive and quantitative methods were used in most LIS research. Survey was the most popular strategy and descriptive statistics mostly used for data analysis.

2.3 Summary

During the last years, the topical structure of LIS has been analyzed almost solely by scientometric methods. Only one of the eight studies used content analysis. The topical structure of LIS varies between studies mostly due to the journal set and publications selected for analysis. The emerging main trend is the decline of library matters as research objects and the proliferation of scientometric topics. IR and information seeking have been popular research topics, but slightly losing in popularity. Empirical, descriptive quantitative studies have been the most common in LIS with survey as the most popular strategy.

3. Research questions, data and methods

3.1 research questions.

What was the topical and methodological state of LIS research in 2015?

SQ1a-e: What are the shares of various (a) topics, (b) research strategies, (c) methods of data collection, (d) types of analysis and (e) types of investigation (or contributions) in LIS research in the year 2015?

SQ2a: How have the shares of article types (scholarly vs non-scholarly) evolved from 1965 to 2015?

SQ3a-e: How have the shares of various (a) topics, (b) viewpoints (c) social levels, (d) research strategies and (e) the application of research strategies within topics evolved from 1965 to 2015?

We investigate RQ1 through articles published in core LIS journals in 2015. The research design (concepts and methodology) and data are explained next in sections 3.2 and 3.3 . We examine RQ2 in the light of comparable statistics for the years 1965, 1985, and 2005 and 2015, reporting on the rise and fall of LIS research foci and methodological choices. The paper contributes to our understanding of what LIS is and how it has evolved.

A reliable account of LIS research based on publications requires data that includes all or a representative sample of research publications in the field. One has to define LIS to tell the difference to other disciplines, what characterizes research for excluding non-research publications and how to identify publications in LIS among all other publications.

Although it is difficult to find a definition of LIS satisfying all scholars, it is widely accepted that the unifying characteristic of LIS is the study on the provision of access to desired information ( Vakkari, 1994 ). However, this brief characterization is challenging to operationalize. We set the criteria for what constitutes LIS research by the classification system for LIS topics ( Appendix 2 ): publications whose topic can be positioned within its classes belong to LIS. This solution has limitations, but using the same, although somewhat revised operationalization across the period examined fosters comparability of findings. Thus, our notion of LIS may not be shared by the entire LIS community, but it produces comparable results on the trends in LIS research.

In several leading journals, many authors come from other fields than LIS. One might suggest that LIS articles could be identified through their authors' disciplinary backgrounds. We think that an article's “LISness” must be determined topically – as being classifiable in the LIS classification and not by authors' background – because LIS is a crossroad of study fields rather than a tightly buttoned discipline.

There is nevertheless one limitation in the above notion of LIS – a rigid topical LIS classification becoming a straitjacket. If the classification does not evolve through omission of old, and/or addition of new (sub)classes over time, one may only analyse changes in relative weights of the originally selected classes. Such an approach does not welcome the evolution of LIS as a discipline and is blind to the growth of knowledge. Our classification of LIS topics originates from the one by Järvelin and Vakkari (1990 , 1993) which they used for the analysis of LIS research in 1965, 1975 and 1985. It is now about 30 years old so it risks becoming anachronous. Therefore, we have revitalized the classification scheme by introducing new subclasses. However, major new developments do not necessarily fit in the classification but accumulate in the catch-all class “Another discipline” (Class A  = 900).

We limit the publications to core scholarly journals in LIS which causes some bias in the results. Not all types of LIS research are equally well represented in journal articles as Sugimoto (2011) has noted. However, research articles form the core of the literature cited in LIS. Moreover, journal articles have been almost the sole source of data in recent studies of LIS research. Our aim to analyse a standard set of scholarly LIS journals over the years enhances comparability of findings. Naturally, we have included new journals under pressing needs and removed others which have ceased to exist (see Appendix 1 ).

Our unit of analysis is an article; we are not trying to describe the content profiles of journals, which function only as intermediate steps in reaching the articles. Therefore, the uneven productivity of journals in the number of articles is not problematic. However, changes in editorial policies of journals, or the in/exclusion of a prolific journal in a subarea (like the journal Scientometrics ) certainly tilt the findings. Tuomaala et al. (2014) tackled this issue by presenting the key findings with and without the data derived through Scientometrics . We include this journal because there are compatible earlier results for 2005 and because there is no reasonable justification for selecting some proportion of articles in Scientometrics to represent this subarea in LIS. Scientometric studies have been considered as part of LIS by several scholars (e.g. Abritzah et al. , 2015 ; Huang et al. , 2019 ).

All journals are in English. This choice, while possibly causing some bias in the findings, is typical in analyses of LIS. The year 2015 volumes were taken as the sources of research articles – because it follows with the same interval (a decade) earlier analyses of LIS utilizing the same approach. Only potential research articles were collected (full articles, brief communications and critical reviews) and other texts (errata, letters-to-the-editors, book reviews, announcements and ads) were excluded. The basis of content analysis of each article was its metadata, i.e. title, abstract and keywords, or title and first page, depending on what was available. If an article proved impossible to analyse based on such metadata, its body text was consulted.

The total number of articles in the data for 2015 is 1,514. We excluded from the analysis articles which were classified as non-LIS studies ( A  = 900) ( n  = 192) and non-scholarly articles ( n  = 112). The number of articles in the main analysis is thus 1,210.

3.3 Methods of content classification

The articles were classified according to eight dimensions (see Appendix 2 ):

Among them, LIS topic and Viewpoint represent the topical content, and Scholarliness indicates research articles that we focus on. The remaining five dimensions represent articles' methodological aspects. We discuss these dimensions, and their modifications briefly below.

Articles' topics were classified using the classification LIS topic . This classification system has been used widely (e.g. Hider and Pymm, 2008 ; Järvelin and Vakkari, 1990 ; Ma and Lund, 2020 ) and contains the following major classes:

020 -The class library history was focused to history of L&I institutions, activities or phenomena

030 - Publishing was extended to cover even archival document and information history

410 -The study of circulation and interlibrary loan activities was generalized to document delivery using documents in any physical forms and delivery means.

440- The study on user education was broadened to information literacy education (incl. information skills).

530- The subclasses of information search and retrieval were extended to cover studies in live collections as well.

534- The class on social media retrieval (e.g. Facebook, Twitter) was added.

We believe that these modifications, in contrast to the study by Tuomaala et al. (2014) , serve maintaining the classification up-to-date and retaining comparability across the datasets.

Each article was classified under a single topical class; in the main classes 400–700, only the sub-classes were used for classification. When an article had many topics, its main topic was identified for classification. For instance, an article on education in information retrieval was classified as education and an article on information retrieval for education as information retrieval. The class “Other aspect of LIS” did not grow unduly large, which hints that it was possible to reliably select the major topic among the sub-topics of articles.

Scholarliness indicates whether the article reports scholarly research or not. The criterion for research is that the article reports on at least somewhat systematic approach to construct new concepts, knowledge and ideas ( Peritz, 1980 ). This means that, typically , some research question is presented, some research method is identified and some results are acquired.

Classification of the viewpoint on information dissemination was based on traditionally recognized actors in the process of information dissemination (author, intermediary, end user, etc.) and their organizations. To classify we ask, whose needs, interests or opinions are analyzed in the study. The class p  = 19 for other viewpoint is a new class in the present paper.

The classification of social level differentiates among the individual, organizational and societal levels, also separating out multi-level analyses. The class individual was used when the objects studied in the article were individuals. For example, a study on intermediary behavior through analysis of search protocols has the social level individual. The class organizational was used when the objects studied in the article were organizations (e.g. library institutions, end-user institutions) even if the informants were individuals. The class societal was used when the objects studied in the article were, e.g. municipalities or societies. “Not applicable” was used when the objects studied in the article were at no level of social organization, as in studies of bibliometric laws or digital collections.

The methodological aspect of a study consists of research strategy , data-collection method , type of analysis and type of investigation . Research strategy is an overall approach to the study within which, for example, the decisions on data collection and the type of analysis are made. Among the typical strategies for empirical research are the historical , survey and qualitative strategy. The other main strategies are referred to as conceptual research strategy (e.g. verbal argumentation or concept analysis), mathematical or logical strategy and system and software analysis and design ( Tuomaala et al. , 2014 ).

In the original classification system of Järvelin and Vakkari (1990) , evaluation and experiment were kept as different research strategies. However, for 2005, they were merged because both surveys and experiments may have elements of evaluation. In the present study they were kept separate. Therefore, the Variable M (Research Strategy) has three new classes: M  = 14 ( evaluation strategy ), M  = 22 ( experiment , incl. field experiment) and M  = 29 ( other empirical strategy as a catch-all for any other qualitative or quantitative strategy), partially overlapping the earlier ones.

In empirical research the data are collected through various data-collection methods. These are listed in the classification Data-collection method . For the present analysis, this variable has two changes: new class C  = 15 ( harvesting databases or their log files) and extended class C  = 20 ( observation , incl. eye-tracking, screen capture, wearable recorders).

The classification Type of Analysis indicates whether the article reports qualitative, quantitative or mixed type of empirical research, or whether it is non-empirical.

Each article was classified under one content class for each variable A, …, I . The data for 2015 were divided evenly between the researchers for classification. For reliability analysis, the researchers reclassified 31 articles independently. Reliability was calculated by means of Fleiss' Kappa, the value of which ranges from −1 for complete disagreement, to ±0 for random choices, and to +1 for complete agreement. Kappa values 0.41–0.60 are moderate, 0.61–0.80 good and 0.81–1.0 very good. The agreement results are in Table 1 . The classifications of Scholarliness, Main topic, Topic, Social level, Type of analysis and Type of investigation have (at least) good agreement, while Viewpoint, Research strategy and Data-collection method have moderate. For p , a major source of inconsistency was p  = 0 vs p  = 17 (no viewpoint vs developer's viewpoint): often disagreement on methodology followed in lieu – Is this paper mathematical, software or empirical, or a bit of all?

A plausible explanation for the moderate reliabilities is that classification of the viewpoint and methodology-related aspects based on article metadata often left much room for interpretation. Even scholarly articles in LIS core journals may be quite scarce in describing, in their metadata, the methods used in the study, and the body text does not always reveal the secret without serious effort unless the methods are well-established with a standard name tag.

4. Findings

4.1 analysis of lis articles, 4.1.1 topics.

Table 2 indicates that S&PC is the most popular major research topic (37.4%) followed by IS&R (22.9%), L&I service activities (13.9%) and information seeking (13.9%). The four most frequently published major topics cover 88.1% of all scholarly articles. Other major topics were notably less popular with analysis of LIS (3.1%) and professions (2.6%) as most frequent research topics. The results show that in 2015 research published in journals accumulated heavily on S&PC, which took almost four out of ten articles.

Most popular sub-topics within the major topics were other aspects of S&PC (14.0%) such as research assessments or various co-word analyses of research specialties. The next popular sub-topics were scientific and professional publishing (12.9%), citation patterns and structures (7.6%). These three most popular sub-topics belong to the area of S&PC.

Within IS&R the most studied sub-topics were digital information resources (5.0%), classification and indexing (4.0) and text-retrieval methods (3.7%). The proportion of interactive, user-oriented IR was only 3.0% of all research output. Neither had social media retrieval stimulated much interest (1.2%) in 2015.

Within the area of information seeking task-based information seeking (3.7%), information management (3.2%) and other types of information seeking studies (2.5%) (e.g. about serendipity) were the most popular sub-topics. Within L&I service activities other L&I service activities (2.8%), collections (2.7%) and user education (2.2%) were the most frequently studied sub-topics.

4.1.2 Research strategies

Empirical research strategy (72.7%) was notably the most popular of research strategies ( Table 3 ). Conceptual (10.5%) and mathematical or logical (8.9%) research strategy were clearly less popular. Among empirical strategies survey (23.9%) was the most common followed by other bibliometric strategy (11.4%), case or action research strategy (9.6%) and experiment (5.9%).

Between the major topics, there were clear differences in methodological orientations. In L&I services survey (37%) dominated followed by the conceptual strategy (18%). Within IS&R mathematical or logical strategy (25%) with experiment (23%) were the most popular strategies. In information seeking survey was the topmost strategy (46%), and then qualitative strategy (17%). In S&PC other bibliometric strategy (29%) and survey (20%) were the most common strategies.

4.1.3 Methods of data collection

Reflecting the two most popular research strategies, survey and other bibliometric strategy, most popular data collection techniques were questionnaire or interview (17.9%) and citation data collection (26.5%) ( Table 4 ). IR experiment (11.3%) and multiple methods of collecting data (11.8%) were also popular techniques.

In studies on L&I services data were mostly collected through questionnaires or interviews (29.8%), by combining several techniques or by collecting items for content analysis (11.3%). IR experiment (48.0%) was clearly the most frequent data collection technique in IS&R followed by combined data collection methods (10.1%) and questionnaires or interviews (7.9%). In information seeking the use of questionnaires or interviews (53.6%) dominated before several data collection methods (20.8%) or other data collection methods (7.1%). In S&PC data were collected in 70.4% of cases by citation data collection methods.

4.1.4 Type of analysis and type of investigation

Quantitative analysis (67.4%) was in the articles the main-stream approach, while qualitative (13.6%) and mixed (5.5%) approaches were small brooks. In IS&R (71.8%) and in S&PC (91.4%) quantitative analysis was almost the sole approach, while in L&I services qualitative analysis (23.2%) had gained footing besides quantitative analysis (47.6%). The same holds in information seeking (32.1% vs 46.4%).

Empirical research (78.9%) was the most common research type ( Table 5 ). The emphasis was on descriptive studies (54.7%), while comparative (16.4%) and explanatory (7.8%) studies were in the clear minority. Among non-empirical studies, the methodological (6.4%), conceptual (4.5%) and theoretical (4.0%) ones were most popular, although their share in the whole population was modest.

Descriptive studies were clearly most common in L&I services (59.5%), information seeking (60.7%) and S&PC (63.7%), while comparative ones (37.9%) in IS&R followed by descriptive studies (32.9%). Interestingly, in information seeking explanatory studies (22.0%) were relatively common, whereas methodological studies (11.3%) had gained some footing among studies on S&PC.

4.2 Analysis of trends in LIS across 50 years

The proportion of research articles among all articles has grown linearly from 30% to 91% during the period studied ( Table 6 ). The growth has been rapid especially during the last 10 years compared to earlier 20-year periods. Tuomaala et al. (2014) is the source for the year 1965, 1985 and 2005 data throughout the section.

This means that even if the roots of LIS may lie in the professions, LIS has matured as a scholarly discipline. While the number of articles in core LIS journals grows notably, even the number of professional articles diminishes significantly – from over 300 in 1965 to just over 100 in 2015. Such articles may be submitted to professional journals reflecting the division of labor between scholarly and professional journals, or the acceptance thresholds may have become more stringent.

In the following we focus on trends based on research articles in LIS.

4.2.1 Topics

The most striking development is the rise of S&PC research from about a 5% share in 1965 to close to 40% in 2015 ( Table 7 ). The proportion of S&PC increased strongly in 2005, because the journal Scientometrics was included in the journals studied. Thus, in a sense the rise is technical, although it clearly reflects the popularity of S&PC within LIS; in 2015 it was the most popular broad topic. This is due to the active publication policy of “Scientometrics”. The number of scholarly articles in this journal in 2015 was 344. This was the highest among all journals in the data.

IS&R and L&I service activities have been the largest areas of research from 1965 to 2005, while S&PC has overtaken L&I service activities in 2005 and conquered the largest position in 2015. The share of IS&R – the earlier largest topic – has decreased from 32.4% to 22.9% during the time period observed. The share of L&I service activities has decreased also notably from 25.4% to 13.9%. By contrast, the proportion of studies on S&PC has increased in particular during the last two observation periods. Although the shares of articles in various topics have decreased during the years, their absolute numbers have increased notably. Although, e.g. L&I service activities, is not relatively as popular a topic as it used to be, there is more research on it than earlier. The general expansion of research activities is behind these trends.

Information seeking has been relatively popular research topic during the years. Its share has stabilized around 12–14% in the last two periods. The share of articles in the remaining major topics is substantially smaller varying between 5% and 1%. Less frequent main topics have lost their attraction as research objects within research community. This has happened mostly in the twenty-first century. This diminishing trend has been associated with the growth of the four most studied research areas – L&I services, IS&R, information seeking and S&PC. Their proportion has increased from 73.4% to 88.1% between the years.

4.2.2 Most frequent topics

There is a clear change in the composition of the six most popular topics during the observed period ( Table 8 ). The minor topics and sub-topics in L&I services have lost their popularity, while sub-topics in IS&R and S&PC have gained it especially after 2005.

In 1965 there was a strong emphasis on classification and indexing (21.8%), but also automation (7.8%), collections (6.3%), methodology (7.8%) and analysis of LIS (5.6%) attracted attention. In 1985 problems of IS&R gained diversified attention focusing on IR (12.7%) and classification and indexing (5.6%). L&I services were represented in top six topics by collections (7.1%) and administration (5.8%). In 2005 reflecting the popularity of IS&R, interactive IR (7.7%), classification and indexing (7.1%) and web retrieval (4.6%) were among the top six topics. S&PC had gained also popularity so that other topics in S&PC (10.4%) and citation structures (6.5%) had penetrated among topmost topics. In 2015 the three top positions were conquered by the sub-topics of S&PC – other S&PC (14.0%), scientific publishing (12.9%) and citation structures (7.6%). IS&R was represented also by three topics, digital information resources (5.0%), classification and indexing (4.0%) and text retrieval (3.7%).

4.2.3 Viewpoint on information dissemination

During the years, it has been very typical to analyze research objects regardless of the viewpoint on information dissemination ( Figure 1 ), in studies on IS&R and S&PC in particular. This is likely due to active technological development foci in information interaction and process-neutrality of many scientometric studies.

The change from intermediary's viewpoint to end-user's and developer's viewpoint has occurred in the period observed. Intermediary's angle was dominant in 1965 and 1985 (35% and 37% respectively), while end-user's (12% and 10%) and developer's (7% and 6%) viewpoints were minor ones in this period. Producer's angle was also at its highest in 1985. The dominance of intermediary's and producer's viewpoints may reflect the active development of the online industry and intermediary's profession at that time.

Not until 2005 onwards the end user's and developer's viewpoints bypassed the intermediary's position. In 2015 developer's angle with the share of 19% was somewhat more popular than the end user's angle with the share of 17%. The gradual change of focus from intermediary's perspective to end-user's and developer's perspectives reflects the changes in the emphasis of research from L&I services (intermediary) to IS&R (developer) and information seeking (end-user).

4.2.4 Social level

Studies focusing on individuals have a growing trend, more than doubling from 13% to 28% during the period observed ( Figure 2 ). The organizational level of analysis peaks in 1985 (26%) and declines thereafter. These trends reflect the changing focus of research from L&I services (organizations) to information seeking (individuals). Society level or multi-level analyses are infrequent (under 10% share) during the whole period. The clearly dominant class is “not applicable”, with a share over 50% almost the whole period, signaling the popularity of artifacts and ideas as objects of study in LIS.

4.2.5 Research strategies

Empirical studies dominate LIS – their trend is rising from around 50% in the early years to over 70% in the later years ( Table 9 ). Of the other major strategies, the conceptual strategy starts at close to 30% share in 1965 but shrinks to 10% by 2015. The math/logical strategy has minor popularity and a flat trend initially but gains a lot for the final year (8.9%). This may be due to the popularity of machine learning and data mining in IS&R. Constructive systems-oriented articles peak in 1985 (14.5%) and follow a declining trend after that, ending at 5% in 2015.

Among empirical strategies survey has been through the years the most popular one with a share about one-fourth, while bibliometric methods have gained footing during the two last periods reaching the share of about 15%. The increase in the proportion of studies on S&PC is naturally reflected in the growing share of bibliometric methods. Evaluation has been uniformly popular with a share of tenth until 2015 when its share drops to 2.6%. Qualitative, case/action research, content analytic, bibliometric strategies and experiment all have a rising trend. Case studies and experiments in particular, have increased their popularity. Diverging from the previous, historical method has a downward trend from 10.6% in 1965 to 1.6% in 2015.

4.2.6 Data collection methods

Questionnaire or interview are popular through the years, other data collection methods and historical method lose their popularity, while citation analysis, IR experiment and multiple data collection methods gain popularity ( Table 10 ).

Questionnaires/interviews are employed by one in six studies through the years, which makes it as a constantly common data collection method. The popularity of other methods varies over time. Content collection for analysis (0% → 6%), citation collection for analysis (0% → 27%) and multiway data collection (2% → 12%) all gain in popularity. The popularity of citation analysis in 2015 reflects the growing share of studies on S&PC among the articles. The declining trend of IR experiment likely reflects the decreasing proportion of articles on IS&R.

4.2.7 Application of strategies in topics

Table 11 shows the popularity of research strategies in the four main topical areas. In L&I service research, survey in particular, but also conceptual strategy has been dominant over the years. Survey has also been the most popular strategy in information seeking, although its share has declined from 78% in 1965 to 46% in 2015. This decline is associated with the increasing use of qualitative strategy in this topic. In IS&R, evaluation/experimentation and software analysis/design are persistent in popularity with a joint share of 37%–77% in the last three periods. This corresponds to the wide-spread view that “evaluation is the hallmark of IR”. Citation analysis/other bibliometric strategy and survey dominate the fourth main area, S&PC (47%–54% since 1985).

5. Discussion

In the 50 years observed both the proportion and absolute number of research articles in core journals of LIS have increased notably. When in 1965 30% of the articles were scholarly in nature, the respective figure in 2015 was 91%. This hints to the maturation of LIS as a field of research, which may also reflect possibly tightened acceptance requirements of journals. The large growth in absolute number of research articles means that also the research activity in LIS in general has increased. Although the proportion of some research topics like L&I service activities decreases compared to other topics, still the research activity in that topic may be greater than earlier.

5.1 Research question RQ1

In 2015, S&PC took the lion's share in the topical profile of LIS, 37.4% of all publications. IS&R came second (22.9%) followed by L&I services (13.9%) and information seeking (13.9%). Thus, there were clear differences in popularity between the major topics in LIS. The other major topics in the classification like professions, library history or analysis of LIS were much less frequent areas covering a few percentages each.

The popularity of S&PC reflects the growing interest in research community toward scientometric analyses. This has led to an increase in the number of articles, e.g. in the journal Scientometrics . It covers 344 articles in our data, which is the largest share among journals observed. Removing Scientometrics from the analysis would change the rank of S&PC from first (37.4%) to fourth (13.6%), and rank IR (31.2%), L&I service activities (19.4%) and information seeking (19.4%) one step higher. However, S&PC has been an essential part of LIS at least from the time of Eugene Garfield and Institute for Scientific Information founded in 1950s. Therefore, it is justified to include a journal representing this sub-field of LIS in the data.

In little over one-half of the studies it was neither possible to identify a viewpoint to information dissemination nor the social level. The former was in part due to the large share of publications in S&PC without viewpoint. Typical in these articles was an emphasis on structural relationships rather than problems faced by some actors. The latter was due to a large proportion of articles in S&PC and IS&R without social level. This is likely due to the popularity of research on technical solutions in the field, ignoring a social level of analysis.

The most frequent viewpoints were developer's, end-user's and intermediary's viewpoints. Intermediary's viewpoint (52.4%) was emphasized in studies on L&I services, whereas developer's viewpoint (48.7%) was emphasized in studies on IS&R, and end-user's viewpoint (57.7%) in studies on information seeking. The most common social level in articles was the individual's (28.4%), followed by the organizational (11.4%). Individual level was most popular in studies on information seeking, while L&I services were, unsurprisingly, approached from the organizational perspective.

Empirical research strategy (72.7%) was the dominant one, while conceptual (10.5%) and mathematical/logical (8.9%) strategies were clearly less popular. Among empirical strategies, survey was the most common (23.9%) followed by other bibliometric strategy (11.4%). There were clear differences in methodological orientations between topics. Within IS&R, mathematical/logical strategy (25%) with experiment (23%) were the most popular strategies, while in information seeking, survey (46%) and qualitative strategy (18%) flourished. Reflecting the two most popular research strategies, survey and other bibliometric strategy, the most popular data collection methods were questionnaire or interview (17.9%) and citation data collection (26.5%).

5.2 Research question RQ2

The trends in LIS research observed in the model study ( Tuomaala et al. , 2014 ) have either strengthened or stabilized. During the last period, S&PC has become the largest research area in LIS with a share of about 37%. The largest research topic until 2005 was IS&R. Its share has declined from about 30% until 2005 to 23% in 2015. L&I services have lost their strong position especially in 2005 and 2015 to the level of 14%, while information seeking has increased and stabilized its share to around 14%. For 50 years, research in LIS has increasingly accumulated on these four major topics from about 75% to 90%. Among the six most popular sub-topics a similar 50-year accumulation can be observed. The most popular topics come increasingly from S&PC or IS&R.

The findings about the largest research areas in LIS are in line with earlier studies, which confirm the earlier findings. A more detailed comparison of sub-topics is not possible due to the differences in categorizing topics and sub-topics. Several studies, however, indicate a decline in the share of studies on L&I services, while reporting a steady growth in studies on S&PC, or a declining stabilization in studies of IS&R. S&PC is the largest research area followed by IS&R ( Chang et al. , 2015 ; Figuerola et al. , 2017 ; Han, 2020 ; Ma and Lund, 2020 ). Ma and Lund (2020) also report findings corresponding to ours that the top research method in IS&R is experiment, in L&I services survey and in S&PC citation analysis.

The topical changes are reflected in other aspects of research too. Intermediaries' and their organizations' viewpoints have yielded to end users' and their organizations' viewpoints. During the last period, in particular the developer's view has strengthened in research. In addition, studies on the individual level of analysis have gained footing, while organizational or societal level in analyses has diminished. This reflects the increase in end-user searching and in the use of tools designed for that purpose.

The role of empirical research strategy has strengthened in LIS. Although survey has a constant major role, an increase in the use of bibliometric methods and case studies has enhanced the position of empirical research strategies. Citation data collection has become the most popular data collection method, reflecting the popularity of S&PC as a research topic. IR experiment as data collection method has lost some of its standing due to the shrinking share of IS&R research. A positive sign has been the growing use of multiple methods in data collection. It seems that pluralism in the use of research strategies has strengthened. While in 2005 in three major research topics one research strategy dominated, in 2015 this held only in one topic. Thus, the research strategies had distributed more evenly across major topics than earlier. In all, our results suggest, that although research in LIS has accumulated in major research areas, versatile use of research methods has increased in these areas. As a consequence, the account of research objects may have become more comprehensive.

The findings in this study help planning teaching in LIS by indicating continuously active and currently hot topics of research and ways of approaching them methodologically. – For example, experimental design and citation analysis seem useful skills. In addition, the findings, and our approach to content analysis help research development through identifying, which topics have (not) been studied and which approaches have (not) been employed. In fact, the classification system for content analysis can be seen as a multi-dimensional study design generator: each cell is a junction of topical and methodological choices.

A field of research may institutionalize both cognitively and socially ( Whitley, 1984 ). The former means a shared and coherent understanding of principal research problems and goals, ways of conceptualizing the research objects and methodologies to study them. The latter refers to, e.g. university departments, journals and conferences representing the field of research.

It is questionable whether the 50 years have led to cognitive institutionalization in LIS as a whole. There hardly exists a shared understanding of principal research problems and goals. Among the major research topics, the professionally motivated core, L&I activity research, shrinks and fosters an endemic viewpoint (of intermediaries). In the other three major topics, the end-user's, developer's and no viewpoint are more common. This may indicate that the major thrust of R&D in dissemination of information is around dissemination-process reengineering, not on its historical (or contemporary) forms. The other three major areas rather work with strongly institutionalized external disciplines than develop shared research problems, goals and approaches – S&PC with Science Studies, IS&R with Computer Science (cf. Chang, 2018 ) and Information Seeking with application fields like Medicine or Health Sciences ( Deng and Xia, 2020 ) or Communication Studies and social sciences more generally. These other disciplines do not necessarily share the research goals of LIS sub-fields. It may be that the growing interest of other disciplines toward research problems relevant to LIS combined with the growing interest of some major journals in the field to publish papers belonging to the adjacent fields disintegrates LIS. Interdisciplinarity is fruitful in developing new knowledge, but isn't here a risk for LIS being absorbed by the stronger partners – both cognitively and socially? Further study is needed to analyse these questions.

5.3 Limitations

The dataset and type of analysis of the present study bring some inherent limitations to the findings, which are in part discussed in the section on data. It is debatable how representative the (selected) core scholarly LIS journals are for LIS research. There are also conferences that could be sources of LIS papers and therefore change the relative shares of some topics. The frequencies of topics do not inform about the content and evolution of research labeled by the topic names. Finally, the moderate reliability values of some variables hint to care required in interpreting our results.

6. Conclusion

Research in LIS has experienced structural changes during 1965–2015. In the topical profile of LIS, the earlier strong emphasis on L&I services has declined notably, while S&PC has become the most popular topic. IS&R has kept its essential position, although its relative popularity has decreased during the last ten years analysed. Individuals are increasingly, but organizations decreasingly, the units of observation in the studies. Moreover, intermediaries' view on information dissemination process has yielded to end-user's view and recently to developer's view. These changes are due to reorientation in LIS from L&I service organizations to information seeking and searching by individuals and development of tools for this.

Methodological changes also reflect changes in LIS topics. The popularity of empirical research strategies has grown, while conceptual strategies have lost interest. Survey, scientometric methods, experiment, case studies and qualitative studies have gained in popularity, while historical method has lost. Survey is an all-round strategy popular in information seeking, S&PC and L&I services. Experiment is typical in IS&R, while scientometric methods are typical in S&PC. Interestingly, although research in LIS is topically accumulating increasingly into four major research topics, methodologically it is scattering increasingly into many approaches. Consequently, it is likely that LIS research has created a more versatile and valid account of its research objects during the years analysed.

Views on the information dissemination phase in 1965–2015 (%)

The social level in 1965–2015 (%)

Analysis of classification reliability – Fleiss' Kappa for two classifiers

Topics of LIS in 2015 ( n  = 1,210) (%)

Research strategies in 2015 ( n  = 1,210) (%)

Data-collection methods in 2015 ( n  = 1,210) (%)

Investigation types in 2015 ( n  = 1,210) (%)

Article types in 1965–2015 (%)

Main topics in 1965–2015 (%)

The six most popular topics in 1965–2015 (%)

Research strategies in 1965–2015 (%)

Data-collection methods in 1965–2015 (%) (* indicates < 0.5%)

The three most popular research strategies for topics in 1965–2015 (%) (* indicates n  < 10)

List of journals in the data set

Appendix 2: The classification scheme

Only numeric codes in italics were used in coding.

LIS TOPIC – A

010–030 research on lis context.

010-the professions (of librarians, intermediaries)

020 - library history, history of L&I institutions

030 - publishing (analyses of, incl. history)

100–300 Research on LIS studies

100- study on education in LIS Studies (studies on LIS itself, see 300)

200-methodology (study of research methods; for work task performance, see 400 … 600)

300 - analysis of LIS discipline (also LIS subareas)

400 Research on LIS service activities

410 -study on document delivery (incl. circulat-ion, interlibrary loans of docs in any forms)

420 - collections study (of any media, e.g. ebooks)

430- study on information or reference service

440- study on user education or information literacy education (incl. info skills)

450- study on L&I service buildings and facilities

460- study on administration or planning (incl. L&I service visions and policies)

470- study on automation or digital libraries (if no L&I service context, consider 540)

480- study on other L&I services (incl. school libraries; library's public)

490 -study on several interconnected activities

500 Research in information storage and retrieval

510- study on metadata/cataloguing (metadata for any type of docs)

520 -study on classification and indexing (content of any media objects; using any intellectual and automatic means)

530 study on information search and retrieval (clustering, filtering, recommendation, query formulation, retrieval models, QA, searching, summarization – in live or test collections, without user participation)

531- study on text retrieval methods (in live or test collections; incl. CLIR)

532- study on retrieval methods in other media (image, video, music, …, multi and hypermedia; if focus on WWW, then 533)

533- study on web retrieval methods

534- study on social media retrieval methods

540- study on digital information resources (e.g. various types of databases (incl. data journals; repositories – focus on general props and use)

550 -study on interactive (user-oriented) IR

560 -study on other aspects of IR (incl., QA, archival IR system design; spoken queries)

600 Research on information seeking

610 -study on information dissemination (professional, work and everyday life contexts)

620 -study on the use or users of channels or sources of information (focus on channels or sources; manual or digital)

630 -study on the use of L&I services (no other channels considered)

640 study on Information seeking behavior (focus on persons)

641- study on task-based information seeking (tasks or interests as points of departure; incl. everyday-life tasks and info practices)

642-other type of information seeking study (ex: presence in social media sites; serendipity)

650- study on information use (whether and how)

660 -study on information management (incl. IRM, knowledge management and sharing)

700 Research on scientific and professional comm

710- study on scientific/professional publishing (incl. reviewing)

720- study on citation patterns and structures

730- study on web-metrics (incl. alt-metrics)

740- study on other aspects of scientific or professional communication

800- study on other aspects of LIS (e.g. task analysis, overview of library scene)

900- study in another discipline on LIS forum (may be relevant but focus is outside LIS)

SCHOLARLINESS -- R

0 -not research

1- research

VIEWPOINT ON DISSEMINATION -- P

10- study on several interconnected phases of dissemination

11- information producer ' s viewpoint

12- information seller ' s (marketer's) viewpoint

13-intermediary's viewpoint

14-intermediary organization's viewpoint

15-end-user's viewpoint

16-end-user organization's viewpoint

17- viewpoint of the developer of the process or a service

18- LIS educator's viewpoint

19-other viewpoint

00 - no viewpoint on information dissemination

SOCIAL LEVEL -- S

1- individual

2- organizational

3- societal

4- multi-level

0- not applicable

RESEARCH STRATEGY -- M

10 empirical research strategy.

11 - historical strategy

12-survey strategy (typically quant analysis, but may include qual studies)

13-qualitative strategy (prefer M  = 14–16)

14 - evaluation strategy

15-case or action research strategy (incl. critical incident)

16-content or protocol analysis (both qual and quant; incl. discourse analysis)

17 - citation analysis

18-other bibliometric strategy (incl. co-authorship anal)

21 - secondary analysis

22-experiment (incl. field experiment)

29-other empirical method (catch-all for any other qual or quant strategy)

30 conceptual research strategy (non-empirical)

31- verbal argumentation , criticism

32-concept analysis (incl. terminology analysis)

40–90 strategies for other non-empirical studies

40-mathematical or logical strategy (focus in formal definition)

50- system and software analysis and design (constructive)

60- literature review (research if analytical)

80 - bibliographic strategy

90-other strategy (incl. devel. of a method)

00 -not applicable, no strategy

DATA COLLECTION METHOD -- C

10-questionnaire, interview (incl. structured and semi-structured)

15-harvesting databases or their log files (incl. social media sites)

20-observation (incl. eye-tracking, screen capture, wearable recorders)

30-thinking aloud

40-text/item collection for content analysis

50-citation data collection (e.g. co-authorship and co-citation data, altmetric data)

60 -historical source analysis

70 - several methods of collecting

80- use of data collected earlier

85 -IR experiment

90-other method of collecting (diary; crowdsourcing; other test )

00 -not applicable (if study is not empirical)

TYPE OF ANALYSIS – Q

1 - qualitative

2 - quantitative

3-mixed types

0 -not applicable (not empirical, not scholarly)

TYPE OF INVESTIGATION -- I

10 empirical.

11-descriptive (incl. historical)

12-comparative

13 explanatory (building/testing theory)

20–50 non-empirical contribution

20-conceptual (incl. terminological)

30-theoretical (without direct data collection)

40-methodological

50-system design (constructive)

90–00 for other empirical, non-empirical and no contributions

90-other type (examples: review; plan/design)

00 -not applicable, not a research article

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Theoretical Backbone of Library and Information Science: A Quest

This study primarily aims to identify unique theories and specific uses of theories in the library and information science (LIS) domain. It provides a comprehensive list of the theories used in LIS journal articles indexed by Scopus (an abstract and citation database) from 1970–2021. It expands on the most common theories and highlights the areas and purposes for which used theories in the LIS domain. Our goal is to demonstrate the usages and applications of various borrowed theories from complementary disciplines in the LIS domain. A systematical methodology is applied, following a few open-source AI-based software packages (such as ASReview, and OpenRefine), to analyse the theories against different parameters, keeping in mind the drawbacks of the previous studies. The study's findings show that the LIS domain's theoretical foundations are understudied. Researchers mainly borrowed theories from social sciences such as sociology, psychology, and management studies to solidify their domain. The paper provides a clear road map for the theoretical development of LIS research. And the resulting outputs may help policymakers, academicians, and researchers, irrespective of disciplines in general and information science in particular, understand the foundations and theoretical and methodological trends of theories that may lead to a better understanding of the theories before their selection and applications.

Author Biographies

Department of Library and Information Science, Associate Professor

Department of Library and Information Science, Professor

Copyright (c) 2023 Bijan Kumar Roy, Parthasarathi Mukhopadhyay

This work is licensed under a Creative Commons Attribution 4.0 International License .

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Forecasting the future of library and information science and its sub-fields

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Forecasting is one of the methods applied in many studies in the library and information science (LIS) field for numerous purposes, from making predictions of the next Nobel laureates to potential technological developments. This study sought to draw a picture for the future of the LIS field and its sub-fields by analysing 97 years of publication and citation patterns. The core Web of Science indexes were used as the data source, and 123,742 articles were examined in-depth for time series analysis. The social network analysis method was used for sub-field classification. The field was divided into four sub-fields: (1) librarianship and law librarianship, (2) health information in LIS, (3) scientometrics and information retrieval and (4) management and information systems. The results of the study show that the LIS sub-fields are completely different from each other in terms of their publication and citation patterns, and all the sub-fields have different dynamics. Furthermore, the number of publications, references and citations will increase significantly in the future. It is expected that more scholars will work together. The future subjects of the LIS field show astonishing diversity from fake news to predatory journals, open government, e-learning and electronic health records. However, the findings prove that publish or perish culture will shape the field. Therefore, it is important to go beyond numbers. It can only be achieved by understanding publication and citation patterns of the field and developing research policies accordingly.

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Introduction

Price ( 1963 p. 19, 1974 , p. 166), predicted more than half a century ago that if the exponential growth of big science continued, we could have two scientists for each person and dog in the population in the future, and we could have one million academic journals by the 2000s. Today, an average of 2.3% of worldwide gross national product is devoted to research and development activities (World Bank 2018 ), and 8.5 out of every 1000 workers is employed as a researcher (Organisation for Economic Co-operation and Development 2020 ). The current total number of active journals published worldwide is 380,299 ( ULRICHSWEB Global Serials Directory 2020 ), and at least the 73,299,923 articles have been published since Price published Little Science, Big Science in 1963. Footnote 1 One of Price’s biggest concerns was that if the growth of big science continued in this way, there would be no scientist who would be able to read every paper ( 1974 , p. 165). Even though we have not reached the number of journals estimated by Price, scientific outputs have still been increasing rapidly, and science is more difficult to follow than ever. In fact, the 90% of the research papers are never cited, and 50% of published research papers are never read by anyone else than the authors, reviewers and editors (Tripathy and Tripathy 2017 , p. 198).

One of the most important problems caused by big science is the inequality of scientific practices in various fields. Big science requires large budgets, diverse research groups with numerous staff members and big laboratories. The high costs of big science create a continuous interplay between the status system, which depends on honour and esteem, and class (Merton 1968 , p. 57). According to Allison and Stewart ( 1974 , p. 599), several publications and citations are affected by this inequality. One of the problems that creates this inequality is disciplinary differences: authors’ productivity depends on their work discipline, popularity and experience (Allison 1980 ; Merton 1968 ). Even today, big science provides a cumulative advantage for some scientists and disciplines. This cumulative advantage, in turn, affects the distribution of science funds (Bol et al. 2018 ) and other scientific career decisions (Petersen and Penner 2014 ). Scientific rewards are much more unequally distributed than other well-being outcomes (Xie 2014 , p. 810). For these reasons, the general characteristics of each discipline should be understood, and decisions should be made according to these characteristics to be able to make the right decisions in research evaluations.

Through the examination of the development of the LIS field, the same inequality can be seen. Over the years, studies have revealed that although the field is relatively small in the social sciences, it has several sub-fields, and the characteristics of these sub-fields are different from each other in terms of publication and citation patterns, authorship structures, production frequencies, etc. (Åström 2010 ; Moya-Anegón et al. 2006 ; White and McCain 1998 ). Besides, the development of sub-fields is directly affected by time and trends. For example, the number of articles written using terms such as ‘information technology’, ‘social network analysis’ or ‘citations’ has increased in recent years, but traditional librarianship topics such as librarianship, archiving or cataloguing have shown a decreasing trend (Larivière et al. 2012 , pp. 1006–1009). While this can be advantageous for some sub-fields, it negatively affects the visibility of more traditional fields and causes an unequal distribution of funds and resources.

The main aim of this study is to determine the sub-fields of the LIS field, reveal the potentials of these fields and make predictions of each sub-field. This will highlight the different scientific practices within the same discipline, which must then be taken into consideration when making decisions. The research questions are as follows:

What is the current structure of the LIS field and its sub-fields? Is there a significant difference between the sub-fields and publication/citation patterns?

Based on a 10-year forecast using the publication information produced in the LIS field, what size increase might be expected in the number of future publications?

Is it possible to predict the number of future citations? What are the citation potentials of the sub-fields?

How will the number of references cited in LIS papers change in the future?

Will the co-authorship patterns in the LIS field change in the future?

Are the quantitative predictions consistent, and do they provide valid insights for the future?

What are the emerging topics of the LIS field? Is it possible to predict future topics of LIS?

Literature review

The literature review is organized into two main parts. The first part presents the subject distribution of papers published in the LIS field which use time series analyses. In the second part, various studies using time series analysis in scholarly communication and research evaluation fields are summarized. The explanation about the use of time series analysis is given in the Methodology section.

Time series analysis studies in LIS

Time series analysis has been applied in the LIS literature to provide forecasts on four different sub-topics: Bibliometrics, health sciences, management and social media. To define main application areas of time series analysis in the field of LIS, 452 papers published in LIS and indexed in Web of Science were evaluated. Footnote 2 (see Fig. 1 ).

Most used keywords of the time series analysis studies in LIS (The sunburst graph was created by using Flourish Studio ( https://app.flourish.studio/ ). Keyword occurrences were calculated by using VOSviewer. Before the calculation, the keyword standardization process was conducted.)

An article by Bates et al. ( 1999 ) is the most cited paper with its 768 citations in the dataset, which includes publications indexed in Web of Science’s Information Science and Library Science category. The article evaluated the impact of computerised physician order entries to reduce the number of medication errors. The authors used prospective time series analysis to calculate the effectiveness of computerised systems for medications. As a result, it is found that computerised systems resulted in a large decrease in medication errors. The second-most cited paper (372 citations) evaluated time series data for online product reviews to understand the effects of word of mouth on online shopping (Li and Hitt 2008 ). The third-most cited article (283 citations) was written by the founder of CiteSpace and his colleagues (Chen et al. 2010 ). The authors used time series analysis to introduce a new multi-perspective co-citation analysis method for information science literature. The most-cited articles from three different sub-topics prove the subject diversity of publications which used time series analysis methods and techniques.

The bibliometric studies using time series analysis are focused on research evaluations, bibliometric indicators and scientometric visualisations. These studies have sought to reveal the relation between early citations and cumulative advantage (Adams 2005 ), evaluate the effectiveness of monetary support systems (Tonta 2018 ), understand the citation trajectories of Nobel prize winners in economics (Bjork et al. 2014 ), visualise or discover the intellectual structure of disciplines (Ma 2012 ) or events (Clausen and Wormell 2001 ), analyse the evolution of research topics (Wu et al. 2014 ), predict citation counts (Abrishami and Aliakbary 2019 ), observe the effects of science policy changes on the number of publications (Baskurt 2011 ), forecast research activities (Bildosola et al. 2017 ), detect emerging/leading papers (Iwami et al. 2014 ) and evaluate research metrics (Liu and Rousseau 2008 ; Ye and Rousseau 2008 ). The time series analysis techniques have been used in bibliometric studies since the early 1990s, and it is still one of the preferred methods in the literature. The main reason for this choice might be explained by the policymaking mission of research evaluations. Following the impact of research policy changes or detecting number of future citations provide important findings to the policymakers to enhance evaluation processes.

Time series analyses have also been used in the papers on health information. In recent years, the studies in health information have focused on evaluating electronic health records, predicting health risks (Perrote et al. 2015 ), optimising drug-drug interaction alert rules using electronic health records (Simpao et al. 2015 ), understanding information-seeking behaviours on health subjects (Huerta et al. 2016 ) and monitoring mental health discussions on Twitter (McClellan et al. 2017 ). The whole world has witnessed how long- and short-term predictions on health issues important during COVID-19 times. It is expected to see a publication explosion in this field in the future. Studies that make predictions on various issues related to the COVID-19 have started to be published in the literature (e.g. Jiang et al. 2020 ; Salgotra et al. 2020 ). Although there are many “unknown unknowns” exists about the virus, time series analysis is likely to be more popular among policymakers by providing a range of scenarios (Grogan 2020 ).

Economics and management sub-subjects of LIS field are also conducted research by using time series analysis. The papers have focused on telecommunication infrastructure and its relations to economic growth/activity (Cronin et al. 1991 ; Dutta 2001 ), disseminating economic census data (Zeisset 1998 ) and early detection of an economic bubble (Dmitriev et al. 2017 ). The last subject category, social media, can be accepted as a part of management subject. During the social media age, the predictions on big data (Niu et al. 2017 ; Saboo et al. 2016 ), social media analyses (Luo and Zhang 2013 ; Zhang et al. 2019 ), word of mouth (Li Hitt 2008 ) and election analyses on Twitter (Conway et al. 2015 ) are some of the important research topics.

The thematic diversity of LIS studies that have used time series analyses demonstrates that this is an essential method for scholars working in this field and is not limited to forecasting. In this study, the main aim of using a time series analysis was to make predictions about research outputs for the LIS field.

Prediction types in the field of scholarly communication and bibliometrics

Forecasting the future is one of the most frequently discussed subjects in bibliometrics and research evaluation studies. Predictions are often made to estimate Nobel Prize Laureates by considering publication and citation patterns. The Web of Science group has provided this well-known prediction mechanism for Nobelists since 2002 (Bourke-Waite 2019 ). Since 1970, millions of indexed publications and citations to these papers have been evaluated and estimations made. Until 2019, 50 Nobel prize winners who were on the list of citation laureates won the Nobel Prize. Of these, 29 researchers received the prize within 2 years of being nominated. Besides the Web of Science Group, there have been other numerous papers published in the literature to predict Nobel Prize winners (e.g., Ashton and Oppenheim 1978 ; Claes and De Ceuster 2013 ; Siegel 2019 ); however, Gingras and Wallace ( 2010 ) warned against the limits of bibliometric tools for predicting Nobel Prize winners due to the rapid growth of disciplines and the halo effect.

Another important area of predictive research is estimating the future number of publications and citations using different tools, techniques and perspectives. Leydesdorff ( 1990 ) sought to estimate the national performance of EEC (European Economic Community) countries and the US using time series analysis models. He found that it is possible to predict the following year’s publication statistics. In Rousseau ( 1994 ) proposed a double exponential model for first citation processes. He aimed to find a model for first citations, and he suggested two models to predict the total number of articles in a fixed group that would ever be cited. In Burrell ( 2003 ) developed the theory of stochastic models to predict the future citation patterns of individual papers. He found that expected citation count was a linear function of the current number, thus proving the idiom ‘success breeds success’.

Chen ( 2012 ) proposed a theoretical and computational model to predict future citations using three metrics: modularity change rate, cluster linkage and centrality divergence. The results indicate that the model could successfully predict future citations. Also, authors’ collaboration statistics and the number of references were found to be good predictors of global citations.

From the citation perspective, Abbasi et al. ( 2011 ) created a model to identify the effects of co-authorship networks on scholars’ performance. As a result, they recommended using researchers’ networks to predict scholars’ future performance. Tahamtan et al. ( 2016 ) reviewed the literature and presented 28 factors affecting the number of citations, these factors were then sorted into three main categories: paper-related factors (such as quality of papers, document type, etc.), journal-related factors (such as the impact factor or journal’s language) and author-related factors. The authors indicated that it is possible to predict the frequency of citations by considering these factors. Similarly, Chakraborty et al. ( 2014 ) developed a two-stage prediction model that produced better results for highly cited papers, and the authors suggested using this model to predict seminal papers in the scientific fields. The authors indicated that although the publication’s authors and venue are crucial for gathering citations, the features related to the papers’ content are more effective for long-term citation predictions. Another study on estimating the factors affecting the number of citations received by articles published in 12 crime psychology journals showed that author impact might be a more powerful predictor of how many times an article is cited than the venue (journal) of publication (Walters 2006 ).

Brody et al. ( 2006 ) examined the relationship between the number of early downloads and the number of citations received for the publications on Arxiv. The study showed that there was a correlation between early downloads and citation impact. Besides, the longer the period for which downloads were counted, the higher the correlation between downloads and citation impact. The authors concluded that the 2 year citation impact should be estimated using 6 months of download statistics.

One of the most recent studies on citation data and forecasting investigated whether the number of volumes that the journals published affected the impact factors of the journals (Zhang 2020 ). The results showed that if the increase of volumes is consistent and significant, a decrease of impact factors is unlikely.

Unlike the other studies mentioned above, some of the studies in the literature did not aim to estimate the number of citations using different statistical data but rather to predict future technologies using citation data. Small ( 2006 ) proposed using clustering, mapping and string formation to track and predict growth areas in science. Érdi et al. ( 2013 ) developed a new model to detect new technological hot spots by clustering patent citation data. Similarly, the Bass and ARIMA models, which are time series analysis models, were utilised to forecast development trends based on patent data (You et al. 2017 ). Kwon and Geum ( 2020 ) indicated that promising inventions can be identified by considering the number of backward citations as the link with previous knowledge. All these studies demonstrate that time series analysis can not only be used to predict the number of outputs in the literature but also to forecast technological developments.

Considering the number of forecasting studies in the literature, predictions provide important findings for scholars, policymakers and managers working in LIS and its sub-fields. Through these findings, it is possible to develop policies, identify the problematic practices and measure the effects of policy changes.

Methodology

Data structure.

To achieve the aims of the study, an advanced search of the Web of Science core indexes (SCI, SSCI and A&HCI) was conducted on 12 December 2019 using the search string WC = ( “Information science and Library Science” ) AND LA = ( English ) AND PY = (1921–2018) AND DT = ( article ). Although the Information Science and Library Science category is only indexed in SSCI, up to 5000 articles were indexed in SCI and A&HCI but not SSCI. Therefore, all three core indexes were included in the study to cover all studies in the field.

The oldest paper within the author’s subscription limits was from 1921, so that year became the starting point. Since the research was carried out before the end of 2019, the year 2019 was excluded from the scope of the research to avoid manipulation of the data and findings. However, the publication and citation data for 2019 were used to validate the success of the predictions made in this study. Also, only articles written in the English language were considered to avoid manipulation due to document type or language differences.

A total of 123,742 articles were analysed and evaluated within the context of this study. The metadata of all articles was downloaded as tab-delimited text using the Web of Science exporting features. A total of 248 different .txt files were downloaded because of the download limits of Web of Science (500 records per download). Then, all the .txt files were combined using the command prompt. Footnote 3 After creating one data file, a deep data cleaning and unification process was conducted. The main characteristics of the dataset are shown in Fig. 2 .

The main characteristics of the dataset.

The articles in the dataset were published in 174 different journals. To answer the research questions, the dataset was divided into four different sub-fields using social network analysis and clustering methods.

Clustering and determination of LIS sub-fields

Two different networks were created for subject clustering. One was a co-cited journal network and the other was a co-occurrence of keywords network. The creation phases of the networks were:

Co-cited journals The VOSviewer visualisation tool was used to create a co-citation network. Before creating the network, the names of the cited journals were standardised. During the standardisation process, different variations of journal names (e.g., Libr Trends, Lib Trends and Library Trends) and title changes (e.g., American Documentation, JASIS and JASIS&T) were considered. All journal names were unified. As a result, 537,227 sources were listed in our dataset. The limit for the minimum number of citations for a source was set at 20; 11,253 sources met this threshold. The co-citation network shown in Fig. 1 presents the top 1000 co-cited journals in the network.

Co-occurrence network The same standardisation process was used to unify the terms that appeared in the title, abstract and keyword fields. The standardisation process included unification of singular/plural words, abbreviations, noun phrases and synonyms. All keywords and the full counting method were selected to create the co-occurrence network. A total of 71,389 keywords were determined, and 8123 of these appeared at least five times in the etwork. The first 1000 terms are shown in Fig. 3 .

Clustering for journals in the dataset (networks of co-cited journals and keyword co-occurrence)

The main reason for creating two different network maps was to cross-validate the subject distribution of the dataset. Based on the clustering results, five clusters were determined for each network map. The clusters determined by most-occurred keywords were parallel with the co-cited journal network. It provided the opportunity to verify the accuracy of the classification. For both networks, the purple clusters were considered to be part of the green cluster. Therefore, the main subjects were classified into four main clusters for our study: librarianship and law librarianship (traditional library studies), health information in LIS , scientometrics and information retrieval and management and information systems .

Although some authors have argued that the journal citation reports (JCR) subject classification is problematic because it covers management information system (MIS) journals, which are different from other sub-fields (Larivière et al. 2012 , p. 999; Ni and Sugimoto 2011 ), our classification results for this field align with previous studies in the literature (e.g., Moya-Anegón et al. 2006 ; Ni and Sugimoto 2011 ; Tseng and Tsay 2013 ) that the field is generally divided into four sub-fields: information science (including information retrieval and information seeking), library science (practical and research-oriented), MIS and scientometrics. In this study, we also added health information to these classifications.

The main limitation of the classification used in this study was the journal-based approach. Some problems were determined for the journals which publish papers on two or more different topics. For example, the journal Health Information and Libraries was classified into the librarianship and law librarianship cluster by co-cited journal analysis, however, the main subject field of the journal is health libraries (Overview - Health Information and Libraries Journal 2020 ). To avoid that kind of problems, an expert control mechanism was conducted and content information from the articles published in that journal was used to decide the journal’s main focus. Additionally, if a journal was not listed in the network map, the same process was applied. For example, African Journal of Library Archives and Information Science was classified into the librarianship and law librarianship cluster using this method. The distribution of the articles into classes is shown in Fig. 4 .

Distribution of journals into subject clusters

Each of the subject fields has different features even though they are all in the same subject category—LIS. Therefore, it is important to understand the structures of these sub-fields and their potentials. Although the librarianship and law librarianship category contains up to 50% of the articles, it is the field with the lowest citation rate. Furthermore, collaboration is more common for health information in the LIS literature. To understand the differences between the sub-fields, the Kruskal-Wallis test was conducted. The test showed that:

The sub-categories of the articles significantly affect the number of publications that the articles cite ( H [3] = 17951.379, p < 0.001),

The sub-categories of the articles significantly affect the number of times an article is cited ( H [3] = 19807.543, p < 0.001) and

The sub-categories of the articles significantly affect the number of authors per paper ( H [3] = 20557.826, p < 0.001).

The test results demonstrate that even if the study focused on a specific category, the sub-fields of that category could have different structures, and thus, evaluations must consider these differences.

Time series analysis and time series forecasting

Many systems that we use today produce time-based data, which can be used to make various inferences. By using the data produced as a result of observations or experiments, problems with the system can be revealed, and predictions can be made about the future. The systematic approach to answering mathematical and statistical questions posed by time correlations is called time series analysis (Shumway and Stoffer 2006 , p. 1). This method of analysis has been used in various fields, from economics to geographical sciences, and it has a wide range of applications. The literature review section summarized different variations of time series analyses in the LIS literature to achieve different aims.

Forecasting is one method of time series analysis and is used to provide the t + 1 value of future time by evaluating the t number of available observations (Box et al. 2008 , p. 2). The forecasting process includes seven phases: (1) problem definition, (2) data collection, (3) data analysis, (4) model selection and fitting, (5) model validation, (6) forecasting and model deployment and (7) monitoring forecasting model performance (Montgomery et al. 2008 , p. 12). SPSS Statistics 23 (IBM) was used to conduct the model selection, fitting, validation, deployment and monitoring phases of this study.

There are different types of time series data, and this must be considered when choosing the analysis method. The well-known data types in time series analyses are trend data, seasonal data and cyclical variations. As seen in Fig. 2 , our dataset shows a linear trend, and thus the analyses were conducted to predict the future of this trend. The only exception for our data was the citations. Any publication requires a certain period to gather citations, and this period varies from discipline to discipline. The decrease in the number of citations over the last 8 years (Fig. 2 ) indicates that the half-life of citations in the LIS field is 8.3 (Incites Journal Citation Reports 2018 ). To prevent this decline from adversely affecting the results of the forecasting, only citation data up to 2010 were used. Thus, time series forecasting was applied using the period from 1921 to 2010.

Unusual events, disturbances or errors that might affect time series data are known as outliers (Box et al. 2008 , p. 536). There are different methods to remove outliers from the data or to normalise the data to provide strong predictions. Removing or normalising citation data was vital for this study because there were too many extreme values, and without processing the data to remove outliers, it would have been impossible to provide a powerful forecast for research outputs in the LIS field. To achieve this aim, median scores of the number of references and the number of citations per year were used to normalise the data. Additionally, autocorrelation and partial autocorrelation plots were created ( Appendix ).

The results of the forecasting analyses are presented in this section according to the number of publications, number of citations, number of references and number of authors per title.

Number of publications

As shown in Fig. 5 , it is predicted that the number of publications in the LIS field will increase in the future. The average number of English language articles published per year in these 97 years was 1262; however, 50% of these articles were published in the last 20 years. Footnote 4 Thus, an increasing publication pattern can be easily seen in Graph 1 in Fig. 5 (all LIS fields). Forecasting the number of publications for the whole LIS literature produced significant results [ Ljung Box Q (18) = 30.286, df = 18, p = 0.035, ARIMA (0, 1, 0) = 0.539, SE = 0.162, p = 0.001], and according to the results, 3974 publications were predicted for 2019 and 4632 for 2027.

Forecasting for number of publications

Because the expected number of articles for 2019 was estimated at 3974, and most of the articles published in 2019 are indexed in the Web of Science, it was possible to compare the forecast to the actual number of publications in 2019. A total of 4412 English language articles were published in 2019 and indexed before October 2020. Footnote 5 This shows that the number of publications will likely increase beyond the prediction, as that many articles are not expected until 2024 in the time series analysis. However, this number is still within the limits of the upper confidence level. If we follow the upper confidence level of the forecast to estimate the near future, there may be 6069 published articles in 2028. This means that if the upper confidence levels are actualised, a total of 52,807 articles could be published between 2019 and 2028.

Although the forecasting tests for sub-fields produced meaningful results, the data were not sufficient to make predictions. It is possible to follow the data from the trend lines and Ljung-box scores. Results of the analysis suggest that increases are expected in the number of publications that will be produced in all sub-fields. This is evidenced by the fact that the forecasts and the actual numbers are quite similar (see Table 1 ), indicating that estimating the number of publications in the LIS field and its sub-fields is possible using time series analysis.

Number of citations

Approximately 17% of the articles published in the LIS field have received 80% (1,209,824) of the citations for the whole literature. These statistics are important in terms of showing the existence of core articles in the LIS field. It is important to note that some publications receive numerous citations while others do not. Fig. 6 shows the distribution of citations received by sub-fields.

Distribution of citations according to sub-fields

Analysis of the dataset shows that two articles received 10,000 citations. These articles were classified into the sub-fields of management and information systems and health information in LIS. The citation potentials are different for each category. For example, papers published in the sub-field of management and information systems are more likely to be cited than those published in the sub-field of librarianship and law librarianship. One of the main features of citation data is their skewness (Bornmann and Leydesdorff 2017 ), and my dataset was no exception. This skewness makes it difficult to produce accurate forecasts for the number of citations in the future.

In addition to the skewness of citation data, other problems are literature obsolescence and citation half-lives. Since the cited half-life of the LIS field is 8 years, it is not possible to make an accurate prediction using data from the last 8 years. For this reason, forecasting only covered the years 1921–2010, and the last 8 years were excluded.

Fig. 7 presents the forecasting results which show that the most consistent prediction could be obtained by analysing the entire discipline. However, for the field-based analyses, the predictions did not produce meaningful results. The results indicate that half of the publications could be cited 20 or more times per year in the future. Considering that the median number of citations currently is 10 per year, this prediction of a major increase in citation counts is possible. However, it is estimated that the number of citations received in the LIS field might exceed 100,000. If we assume the same upper confidence level as we did for the number of publications, the upper confidence for the total number of citations is estimated to be 141,000. Since the distribution of median values does not offer a linear trend for sub-fields, it is difficult to predict which sub-fields will receive more citations. Furthermore, the half-life may be different for each sub-field. This is one of the factors that makes forecasting difficult. Considering all these factors, future analyses might produce more meaningful results.

Forecasting for citations

Number of references per paper

While the number of references that could be cited in publications was more limited in the past, with the increase in the number of publications, there has also been a significant increase in the number of references made in studies. It is possible to monitor this increase from the trendlines in Fig. 8 . The forecast predicts that a total of 300,000 references will be listed in the LIS literature in 2028. Half of the publications are expected to cite at least 63 sources. In 2018, this number was 47. The tests for forecasting produced significant results, and upper and lower confidence level scores were very close, indicating the accuracy/consistency of the future prediction.

Forecasting of the number of references

Despite the success in forecasting the future number of references, it is difficult to make a similar forecast for the sub-fields because of the differences between the fields and the skewness of the reference/citation data. Although they are in the same main subject category, the citation patterns are different for each sub-field. The average number of references per article is 15 (median = 21) in the field of librarianship and law librarianship, 26 (median = 21) in scientometrics and information retrieval, 30 (median = 26) in health information in LIS and 42 (median = 37) in the management and information systems fields.

Forecasting author collaborations

The average number of authors per paper in the LIS literature is two, and the median is one. Thus, scholars in the LIS field generally prefer to work alone. However, health information in LIS is the most collaborative sub-field of LIS literature. The article entitled ‘Academic domains as political battlegrounds: A global enquiry by 99 academics in the fields of education and technology’ Footnote 6 . is the most collaborative paper with 99 authors. The article is classified as part of the librarianship and law librarianship sub-field in our dataset. The main statistics for authorship patterns are shown in Table 2 .

The forecast results show that the average number of authors per paper is three. In the next 10 years, this number is expected to increase to 3.6. Considering the trendline over the past 97 years, this expected result is reasonable. Fig. 9 presents the forecasts for collaboration patterns in the LIS literature.

Forecasting of collaboration patterns

Possibility of consistent forecasting in the publish or perish world

The analyses above demonstrate the difficulty of predicting research outputs. Every year, the number of publications increases. Since no regular trend can be seen in this increase in the number of publications, any predictions we make today are minimum values for the future. Table 1 presents one example of this. All three graphs in Fig. 10 show the estimated increase per year in comparison with the previous period. The periods are determined by considering the years that had increases in the number of publications.

Forecasts by different periods

Figure 10 demonstrates that the number of publications does not have a regular trend. Thus, there is the possibility that no prediction will accurately forecast the number of future publications. If the trend until 1950 had continued to today, the number of publications in the LIS literature today would be 37,049 (30% of today’s actual number). If the data from 1970 were used, the number would be 76,612 (61% of today’s actual number), and if the data from 2000 were used, the number would be 117,336 (94% of today’s actual number). While it is possible to say that forecasts in recent years have been more accurate, that is, the publication trends have been similar in recent years, the unpredictability should be expected to continue regardless of any changes in research performance evaluation systems. Besides, it should be kept in mind that the number of publications may be indirectly affected by unexpected emerging issues such as COVID-19 that significantly affect the publishing patterns of the authors.

It is difficult to estimate the total number of citations using the data up to 1970 because of the cumulative nature of citations. However, predictions using data up to 2000 produced forecasts that are close to reality. Using data up to 1970, it was estimated that the average number of references per paper would be 67 in 2018. However, the data after 1970 changed the situation. Using the data until 2000, the estimated average number of references per paper in 2018 was 22, while the actual average number of references in 2018 was 51. Thus, the number of references in publications have increased far beyond the predictions made using more recent data.

Forecasting the research subjects

The findings confirm that the entire LIS field will face many more publications in the future with the spread of publish or perish culture. Therefore, the key to following the developments in LIS is to go beyond numbers. Although it is difficult to forecast the potential future of LIS subjects by using just numbers, making inferences by looking at the emerging subjects in recent years is possible. Figure 11 shows the most-used keywords of the papers published in the last two years in the LIS field. Footnote 7

Emerging subjects of the LIS field (Flourish Studio was used to create the radial tree)

Figure 11 shows a network of keywords that includes five clusters. The four clusters are parallel with the classification of this study. However, a new cluster named “COVID-19” has been added to the LIS literature as expected. The emerging subjects of each sub-field are:

COVID-19 All the countries have been fighting with COVID-19 since December 2019. According to WHO’s COVID-19 Global Research Database ( Global Research on Coronavirus Disease ( COVID- 19) 2020 ) a total of 123,959 publications were published from the day of the outbreak to November 7, 2020. The subject is also popular in the LIS field. Social media, fact-checking, governmental responsibilities during the pandemic such as political communication, transparency and participation, digital journalism and fake news are the important subjects of LIS field recently. The cluster proves the importance of LIS research focusing on open and correct information all over the world during the pandemic.

Bibliometrics and information retrieval The keywords of this cluster show that bibliometrics and information retrieval issues converge to each other with the developments of computational techniques. Machine learning, text mining, topic modelling and sentiment analyses are used for bibliometric studies such as content-based citation analyses and digital humanities. Also, scholarly communication subjects like peer-review, societal impact, incentives, predatory journals, language (multilingualism) and rankings are important keywords of this cluster. As indicated in the literature review part, predictions are still important for this sub-field.

Librarianship and law librarianship The effects of COVID-19 is also observed in this cluster (e.g. e-learning, e-resources). Information literacy plays a vital role among researchers, students and the public during COVID-19 times. Therefore, traditional librarianship subjects will be important to solve information problems of individuals in the future. Besides, digitization and preservation of archival materials are the other popular subjects of the cluster.

Health information in LIS Many studies in this sub-field focus on disadvantaged groups in recent years. Studies on inequality, refugees and genders can be evaluated in this content. Also, public access to health information, health communication and electronic health records are popular subjects and related to COVID-19 pandemic.

Discussion and conclusion

The study suggested a forecasting mechanism for research outputs in the LIS field. The main aim of the study was to inform scholars and policymakers about the future of research in this field. Nowadays, articles are often only read by a few people (Eveleth 2014 ; Simkin and Roychowdhury 2015 ; Tripathy and Tripathy 2017 ), and the main purpose of publishing is to achieve a numerical advantage rather than further the development of science. Although many researchers have emphasised that the current system should change, there have not yet been any concrete changes.

First, we revealed that publishing, citation and collaboration patterns differ between the sub-fields in the LIS literature. It is a well-known fact that apples and oranges are incomparable in research evaluations (Johnes and Johnes 1992 ); however, this study shows that it is also difficult to compare apples to each other because there are different types of apples (e.g., red, granny smith, honeycrisp, etc.). According to the results of the study more articles are published in traditional librarianship journals, and these journals tend to be cited less than others in the field. Articles published in the management dimension of the LIS field have greater citation potential than other sub-fields. This explains why management journals tend to have the highest impact factor in JCR among the LIS journals. This study shows the sub-field differences in LIS, and any evaluations based on categories should consider the sub-fields and their different characteristics.

The findings of this study indicate the number of publications and citations will continue to increase each year unless there is a change in research evaluation systems. This could lead to an uncontrollable mass of publications in the LIS field. The upper confidence levels estimated by the forecasting model produced in this study were already realised in 2019, demonstrating that this increase will be huge. However, it is difficult to forecast the future of sub-fields because the publication trends in sub-fields differ greatly from the general framework. If the existing systems continue, the inequality between LIS sub-fields will continue to grow. The meaning of following the current research evaluation systems is that the production of papers will continue to increase, and some of the sub-fields will not be able to benefit from future opportunities due to their disadvantages. For this reason, decision-makers and managers must consider field- and time-based differences in their research evaluation tasks.

One of the important results revealed in this study is the predicted increase in the number of publications, citations and references. Given that evaluations are made using citation data, the growing amount of data will make future evaluations more difficult. For this reason, supporting programmes such as the Initiative for Open Citations, which aims to promote the unrestricted availability of scholarly citation data, may also be useful for managing data in the future.

The results show that a lot of papers which have long reference lists will be produced, they will cite each other, more authors will work together to write papers. However, their contents and levels will be different from each other. Many of the studies have predicted that publishing will change in the future as a consequence of these differences. For example, Priem ( 2013 ) claimed that publishing forms, reward systems, measurement tools and peer-review systems will soon change. Similarly, Waldrop ( 2008 ) and Kendall ( 2015 ) stated that open science will be the new norm and that we will experience many changes to authorship and research evaluation systems in the next years. The predictions for the future of the publishing system is also the subject of the LIS field. This study proves the astonishing diversity of research subjects of the LIS field and tries to show the importance of looking beyond numbers.

The search was conducted on 7 July 2020 using the term PY = (1963–2020) in Web of Science’s core indexes: social sciences citation index (SCI), social sciences citation index (SSCI), Arts and humanities citation index (A&HCI), Emerging sources citation index (ESCI), Conference proceedings citation index (CPCI) and Book citation index (BKCI).

To access papers on/using time series analyses, the search string TS=(“time series” OR “forecasting analys*s” OR ARIMA OR “Exponential smoothing”) AND WC=(“Information science & library science”) was used. The search was conducted on 15 May 2020 using the Web of Science core indexes.

The text files were combined into a single file using the following steps: (1) open command prompt, (2) enter the folder, (3) use the code >> copy *.txt join.txt .

Although half of the articles were published in the last 20 years, using the entire past provides advantages for time series analyses using linear data (Jones 1964 , p. 47). The rate of increase in the number of publications over the years is one of the important factors for the success of the forecast. To provide accuracy on forecasts, the entire 97-year period was used for prediction.

A total of 137 articles were identified as early access. These articles might be covered by volumes/issues published in 2020. The total number of articles excluding early access articles was 4275.

https://journals.sagepub.com/doi/full/10.1177/0266666916646415 .

An advanced search was conducted on 5 November 2020 using the search string WC= ( “Information Science and Library Science” ) AND PY = (2019–2020) to gather the publication data for the last two years. All Web of Science indexes including ESCI, CPCI and BKCI were included to be able to cover more papers. Articles, reviews and proceedings were considered. A total of 13,856 papers were evaluated to analyze the emerging subjects of the field.

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Acknowledgements

This research was supported by a research grant from the Polish National Agency for Scientific Exchange, NAWA Poland (PPN/ULM/2019/1/00062). I would like to thank Emanuel Kulczycki, Güleda Doğan, Ola Swatek and the anonymous peer-reviewers for their meticulous reading of the paper and their invaluable suggestions.

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Residual autocorrelation function (ACF) and residual partial autocorrelation function (PACF) plots

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Taşkın, Z. Forecasting the future of library and information science and its sub-fields. Scientometrics 126 , 1527–1551 (2021). https://doi.org/10.1007/s11192-020-03800-2

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Received : 11 July 2020

Accepted : 16 November 2020

Published : 17 December 2020

Issue Date : February 2021

DOI : https://doi.org/10.1007/s11192-020-03800-2

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