bibliometrix package provides a set of tools for quantitative research in bibliometrics and scientometrics.
Bibliometrics turns the main tool of science, quantitative analysis, on itself. Essentially, bibliometrics is the application of quantitative analysis and statistics to publications such as journal articles and their accompanying citation counts. Quantitative evaluation of publication and citation data is now used in almost all scientific fields to evaluate growth, maturity, leading authors, conceptual and intellectual maps, trends of a scientific community.
Bibliometrics is also used in research performance evaluation, especially in university and government labs, and also by policymakers, research directors and administrators, information specialists and librarians, and scholars themselves.
bibliometrix supports scholars in three key phases of analysis:
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Data importing and conversion to R format;
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Bibliometric analysis of a publication dataset;
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Building and plotting matrices for co-citation, coupling, collaboration, and co-word analysis. Matrices are the input data for performing network analysis, multiple correspondence analysis, and any other data reduction techniques.
bibliometrix includes biblioshiny: bibliometrix for no-coders
biblioshiny is a shiny app providing a web-interface for bibliometrix.
It supports scholars in easy use of the main features of bibliometrix:
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Data importing and conversion to data frame collection
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Data filtering
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Analytics and Plots for three different level metrics:
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Sources
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Authors
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Documents
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Analysis of three structures of Knowledge (K-structures):
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Conceptual Structure
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Intellectual Structure
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Social Structure
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Please follow the biblioshiny tutorial at the section tutorial of bibliometrix website https://www.bibliometrix.org/
If you use this package for your research, you must cite it.
To cite bibliometrix in publications, please use:
Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.
Official website: https://www.bibliometrix.org
CRAN page: https://cran.r-project.org/package=bibliometrix
GitHub repository: https://github.com/massimoaria/bibliometrix
Tutorials
How to use: https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html
Data importing and converting: https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html
Stable version from CRAN
Developers version from GitHub
Load bibliometrix
library('bibliometrix')
#> Please note that our software is open source and available for use, distributed under the MIT license.
#> When it is used in a publication, we ask that authors properly cite the following reference:
#>
#> Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis,
#> Journal of Informetrics, 11(4), pp 959-975, Elsevier.
#>
#> Failure to properly cite the software is considered a violation of the license.
#>
#> For information and bug reports:
#> - Take a look at https://www.bibliometrix.org
#> - Send an email to info@bibliometrix.org
#> - Write a post on https://github.com/massimoaria/bibliometrix/issues
#>
#> Help us to keep Bibliometrix and Biblioshiny free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)
#>
#>
#> To start with the Biblioshiny app, please digit:
#> biblioshiny()The export file can be read and converted using by R using the function convert2df:
convert2df(file, dbsource, format)
The argument file is a character vector containing the name of export files downloaded from SCOPUS, Clarivate Analytics WoS, OpenAlex, Digital Science Dimensions, PubMed or Cochrane CDSR website. file can also contains the name of a json/xlm object download using OpenAlex, Digital Science Dimensions or PubMed APIs (through the packages openalexR, dimensionsR and pubmedR.
es. file <- c(“file1.txt”,“file2.txt”, …)
## An example from bibliometrix vignettes
file <- c("https://www.bibliometrix.org/datasets/management1.txt","https://www.bibliometrix.org/datasets/management2.txt")
M <- convert2df(file = file, dbsource = "wos", format = "plaintext")
#>
#> Converting your wos collection into a bibliographic dataframe
#>
#> Done!
#>
#>
#> Generating affiliation field tag AU_UN from C1: Done!convert2df creates a bibliographic data frame with cases corresponding to manuscripts and variables to Field Tag in the original export file.
Each manuscript contains several elements, such as authors’ names, title, keywords and other information. All these elements constitute the bibliographic attributes of a document, also called metadata.
Data frame columns are named using the standard Clarivate Analytics WoS Field Tag codify (https://www.bibliometrix.org/documents/Field_Tags_bibliometrix.pdf).
After importing a bibliographic data frame, we can check the completeness of the metadata included in it through missingData().
missingData(M)
The argument M is a bibliographic data frame obtained by convert2df function.
## An example from bibliometrix vignettes
com <- missingData(M)
com$mandatoryTags
#> tag description missing_counts missing_pct status
#> 1 AU Author 0 0.00 Excellent
#> 2 DT Document Type 0 0.00 Excellent
#> 3 SO Journal 0 0.00 Excellent
#> 4 LA Language 0 0.00 Excellent
#> 5 WC Science Categories 0 0.00 Excellent
#> 6 TI Title 0 0.00 Excellent
#> 7 TC Total Citation 0 0.00 Excellent
#> 8 CR Cited References 2 0.22 Good
#> 9 AB Abstract 7 0.78 Good
#> 10 PY Publication Year 9 1.00 Good
#> 11 RP Corresponding Author 10 1.11 Good
#> 12 C1 Affiliation 21 2.34 Good
#> 13 DI DOI 36 4.01 Good
#> 14 DE Keywords 79 8.80 Good
#> 15 ID Keywords Plus 97 10.80 AcceptablemissingData returns a list containing two data frame. The first one, allTags includes the results for all metadata in M. The latter, mandatoryTags, reports the results only for the metadata needed to perform analyses with bibliometrix or biblioshiny.
The column status classifies the percentage of missing value in 5 categories: “Excellent” (0%), “Good” (0.01% to 10.00%), “Acceptable” (from 10.01% to 20.00%), “Poor” (from 20.01% to 50.00%), “Critical” (from 50.01% to 99.99%), “Completely missing” (100%).
The first step is to perform a descriptive analysis of the bibliographic data frame.
The function biblioAnalysis calculates main bibliometric measures using this syntax:
results <- biblioAnalysis(M, sep = ";")The function biblioAnalysis returns an object of class “bibliometrix”.
To summarize main results of the bibliometric analysis, use the generic function summary. It displays main information about the bibliographic data frame and several tables, such as annual scientific production, top manuscripts per number of citations, most productive authors, most productive countries, total citation per country, most relevant sources (journals) and most relevant keywords.
summary accepts two additional arguments. k is a formatting value that indicates the number of rows of each table. pause is a logical value (TRUE or FALSE) used to allow (or not) pause in screen scrolling. Choosing k=10 you decide to see the first 10 Authors, the first 10 sources, etc.
S <- summary(object = results, k = 10, pause = FALSE)
#>
#>
#> MAIN INFORMATION ABOUT DATA
#>
#> Timespan 1985 : 2022
#> Sources (Journals, Books, etc) 281
#> Documents 898
#> Annual Growth Rate % 0
#> Document Average Age 9.19
#> Average citations per doc 37.12
#> Average citations per year per doc 3.454
#> References 43935
#>
#> DOCUMENT TYPES
#> article 862
#> article; book chapter 1
#> article; early access 9
#> article; proceedings paper 26
#>
#> DOCUMENT CONTENTS
#> Keywords Plus (ID) 1918
#> Author's Keywords (DE) 2243
#>
#> AUTHORS
#> Authors 2079
#> Author Appearances 2657
#> Authors of single-authored docs 112
#>
#> AUTHORS COLLABORATION
#> Single-authored docs 121
#> Documents per Author 0.432
#> Co-Authors per Doc 2.96
#> International co-authorships % 36.41
#>
#>
#> Annual Scientific Production
#>
#> Year Articles
#> 1985 2
#> 1986 2
#> 1988 1
#> 1990 1
#> 1992 4
#> 1993 5
#> 1994 4
#> 1995 7
#> 1996 4
#> 1997 3
#> 1998 4
#> 1999 6
#> 2000 3
#> 2001 4
#> 2002 4
#> 2003 5
#> 2004 5
#> 2005 10
#> 2006 13
#> 2007 11
#> 2008 13
#> 2009 18
#> 2010 32
#> 2011 33
#> 2012 27
#> 2013 34
#> 2014 34
#> 2015 62
#> 2016 62
#> 2017 80
#> 2018 81
#> 2019 125
#> 2020 141
#> 2021 47
#> 2022 2
#>
#> Annual Percentage Growth Rate 0
#>
#>
#> Most Productive Authors
#>
#> Authors Articles Authors Articles Fractionalized
#> 1 MERIGO JM 20 KOSTOFF RN 7.77
#> 2 PORTER AL 19 PORTER AL 5.84
#> 3 KOSTOFF RN 16 MERIGO JM 5.42
#> 4 KUMAR S 15 KAJIKAWA Y 4.62
#> 5 KAJIKAWA Y 14 KUMAR S 4.28
#> 6 ZHANG Y 9 KOSEOGLU MA 3.07
#> 7 ABRAMO G 8 SHILBURY D 3.00
#> 8 D'ANGELO CA 8 ABRAMO G 2.58
#> 9 KOSEOGLU MA 8 D'ANGELO CA 2.58
#> 10 YOUTIE J 8 CULLEN JG 2.50
#>
#>
#> Top manuscripts per citations
#>
#> Paper DOI TC TCperYear NTC
#> 1 CHEN HC, 2012, MIS QUART NA 2161 166.23 15.64
#> 2 ZUPIC I, 2015, ORGAN RES METHODS 10.1177/1094428114562629 844 84.40 17.17
#> 3 RAMOS-RODRIGUEZ AR, 2004, STRATEGIC MANAGE J 10.1002/smj.397 667 31.76 3.76
#> 4 VOLBERDA HW, 2010, ORGAN SCI 10.1287/orsc.1090.0503 626 41.73 9.82
#> 5 DAIM TU, 2006, TECHNOL FORECAST SOC 10.1016/j.techfore.2006.04.004 569 29.95 5.67
#> 6 KOSTOFF RN, 2001, IEEE T ENG MANAGE 10.1109/17.922473 387 16.12 2.66
#> 7 NERUR SP, 2008, STRATEG MANAGE J 10.1002/smj.659 353 20.76 3.48
#> 8 MELIN G, 2000, RES POLICY 10.1016/S0048-7333(99)00031-1 336 13.44 2.15
#> 9 MOED HF, 1985, RES POLICY 10.1016/0048-7333(85)90012-5 310 7.75 1.81
#> 10 MURRAY F, 2002, RES POLICY 10.1016/S0048-7333(02)00070-7 301 13.09 2.40
#>
#>
#> Corresponding Author's Countries
#>
#> Country Articles Freq SCP MCP MCP_Ratio
#> 1 USA 146 0.1644 92 54 0.370
#> 2 CHINA 84 0.0946 41 43 0.512
#> 3 SPAIN 72 0.0811 51 21 0.292
#> 4 BRAZIL 65 0.0732 52 13 0.200
#> 5 ITALY 49 0.0552 31 18 0.367
#> 6 UNITED KINGDOM 47 0.0529 22 25 0.532
#> 7 GERMANY 42 0.0473 29 13 0.310
#> 8 AUSTRALIA 31 0.0349 19 12 0.387
#> 9 NETHERLANDS 31 0.0349 20 11 0.355
#> 10 INDIA 26 0.0293 17 9 0.346
#>
#>
#> SCP: Single Country Publications
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#> MCP: Multiple Country Publications
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#>
#> Total Citations per Country
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#> Country Total Citations Average Article Citations
#> 1 USA 8896 60.93
#> 2 SPAIN 2843 39.49
#> 3 UNITED KINGDOM 2143 45.60
#> 4 NETHERLANDS 2110 68.06
#> 5 CHINA 1939 23.08
#> 6 ITALY 1566 31.96
#> 7 GERMANY 1449 34.50
#> 8 JAPAN 1104 46.00
#> 9 SLOVENIA 1100 157.14
#> 10 BRAZIL 1074 16.52
#>
#>
#> Most Relevant Sources
#>
#> Sources Articles
#> 1 TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 97
#> 2 RESEARCH POLICY 83
#> 3 TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 31
#> 4 JOURNAL OF BUSINESS RESEARCH 28
#> 5 SCIENCE AND PUBLIC POLICY 25
#> 6 TECHNOVATION 19
#> 7 JOURNAL OF TECHNOLOGY TRANSFER 12
#> 8 INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT 10
#> 9 INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT 10
#> 10 R & D MANAGEMENT 10
#>
#>
#> Most Relevant Keywords
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#> Author Keywords (DE) Articles Keywords-Plus (ID) Articles
#> 1 BIBLIOMETRICS 234 SCIENCE 146
#> 2 BIBLIOMETRIC ANALYSIS 161 INNOVATION 130
#> 3 CITATION ANALYSIS 62 PERFORMANCE 117
#> 4 INNOVATION 44 IMPACT 116
#> 5 BIBLIOMETRIC STUDY 30 MANAGEMENT 113
#> 6 TEXT MINING 30 KNOWLEDGE 82
#> 7 VOSVIEWER 30 INTELLECTUAL STRUCTURE 75
#> 8 LITERATURE REVIEW 29 TECHNOLOGY 62
#> 9 BIBLIOMETRIC 28 JOURNALS 59
#> 10 CO-CITATION ANALYSIS 28 MODEL 58Some basic plots can be drawn using the generic function plot:
plot(x = results, k = 10, pause = FALSE)
Manuscript’s attributes are connected to each other through the manuscript itself: author(s) to journal, keywords to publication date, etc.
These connections of different attributes generate bipartite networks that can be represented as rectangular matrices (Manuscripts x Attributes).
Furthermore, scientific publications regularly contain references to other scientific works. This generates a further network, namely, co-citation or coupling network.
These networks are analyzed in order to capture meaningful properties of the underlying research system, and in particular to determine the influence of bibliometric units such as scholars and journals.
The function biblioNetwork calculates, starting from a bibliographic data frame, the most frequently used networks: Coupling, Co-citation, Co-occurrences, and Collaboration.
biblioNetwork uses two arguments to define the network to compute:
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analysis argument can be “co-citation”, “coupling”, “collaboration”, or “co-occurrences”.
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network argument can be “authors”, “references”, “sources”, “countries”, “universities”, “keywords”, “author_keywords”, “titles” and “abstracts”.
i.e. the following code calculates a classical co-citation network:
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
All bibliographic networks can be graphically visualized or modeled.
Using the function networkPlot, you can plot a network created by biblioNetwork using R routines.
The main argument of networkPlot is type. It indicates the network map layout: circle, kamada-kawai, mds, etc.
In the following, we propose some examples.
# Create a country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.8)
# Create a co-citation network
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", n=30, sep = ";")
# Plot the network
net=networkPlot(NetMatrix, Title = "Co-Citation Network", type = "fruchterman", size=T, remove.multiple=FALSE, labelsize=0.7,edgesize = 5)
# Create keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "fruchterman", size=T,edgesize = 5,labelsize=0.7)
The aim of the co-word analysis is to map the conceptual structure of a framework using the word co-occurrences in a bibliographic collection.
The analysis can be performed through dimensionality reduction techniques such as Multidimensional Scaling (MDS), Correspondence Analysis (CA) or Multiple Correspondence Analysis (MCA).
Here, we show an example using the function conceptualStructure that performs a CA or MCA to draw a conceptual structure of the field and K-means clustering to identify clusters of documents which express common concepts. Results are plotted on a two-dimensional map.
conceptualStructure includes natural language processing (NLP) routines (see the function termExtraction) to extract terms from titles and abstracts. In addition, it implements the Porter’s stemming algorithm to reduce inflected (or sometimes derived) words to their word stem, base or root form.
# Conceptual Structure using keywords (method="CA")
CS <- conceptualStructure(M,field="ID", method="MCA", minDegree=10, clust=5, stemming=FALSE, labelsize=15, documents=20, graph=FALSE)
plot(CS$graph_terms)
plot(CS$graph_dendogram)
The historiographic map is a graph proposed by E. Garfield to represent a chronological network map of most relevant direct citations resulting from a bibliographic collection.
The function histNetwork generates a chronological direct citation network matrix which can be plotted using histPlot:
# Create a historical citation network
histResults <- histNetwork(M, sep = ";")
#>
#> WOS DB:
#> Searching local citations (LCS) by reference items (SR) and DOIs...
#>
#> Analyzing 62644 reference items...
#>
#> Found 422 documents with no empty Local Citations (LCS)
# Plot a historical co-citation network
net <- histPlot(histResults, n=20, size = FALSE,label="short")
#>
#> Legend
#>
#> Label
#> 1 MOED HF, 1985, RES POLICY DOI 10.1016/0048-7333(85)90012-5
#> 2 HOFFMAN DL, 1993, J CONSUM RES DOI 10.1086/209319
#> 3 PORTER AL, 1995, TECHNOL FORECAST SOC DOI 10.1016/0040-1625(95)00022-3
#> 4 WATTS RJ, 1997, TECHNOL FORECAST SOC DOI 10.1016/S0040-1625(97)00050-4
#> 5 KOSTOFF RN, 2001, IEEE T ENG MANAGE DOI 10.1109/17.922473
#> 6 VERBEEK A, 2002, INT J MANAG REV DOI 10.1111/1468-2370.00083
#> 7 RAMOS-RODRIGUEZ AR, 2004, STRATEGIC MANAGE J DOI 10.1002/SMJ.397
#> 8 DAIM TU, 2006, TECHNOL FORECAST SOC DOI 10.1016/J.TECHFORE.2006.04.004
#> 9 SCHILDT HA, 2006, ENTREP THEORY PRACT DOI 10.1111/J.1540-6520.2006.00126.X
#> 10 CASILLAS J, 2007, FAM BUS REV DOI 10.1111/J.1741-6248.2007.00092.X
#> 11 NERUR SP, 2008, STRATEG MANAGE J DOI 10.1002/SMJ.659
#> 12 PODSAKOFF PM, 2008, J MANAGE DOI 10.1177/0149206308319533
#> 13 KAJIKAWA Y, 2008, TECHNOL FORECAST SOC DOI 10.1016/J.TECHFORE.2007.05.005
#> 14 LANDSTROM H, 2012, RES POLICY DOI 10.1016/J.RESPOL.2012.03.009
#> 15 FAGERBERG J, 2012, RES POLICY DOI 10.1016/J.RESPOL.2012.03.008
#> 16 SHAFIQUE M, 2013, STRATEGIC MANAGE J DOI 10.1002/SMJ.2002
#> 17 ZUPIC I, 2015, ORGAN RES METHODS DOI 10.1177/1094428114562629
#> 18 MERIGO JM, 2015, J BUS RES DOI 10.1016/J.JBUSRES.2015.04.006
#> 19 LAENGLE S, 2017, EUR J OPER RES DOI 10.1016/J.EJOR.2017.04.027
#> 20 VALENZUELA L, 2017, J BUS IND MARK DOI 10.1108/JBIM-04-2016-0079
#> Author_Keywords
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 BIBLIOMETRICS; CITATION; CO-CITATION; CO-OCCURRENCE; CO-WORD; DECISION AIDS; PATENT CITATION; RETROSPECTIVE ANALYSES; ROADMAPPING; ROADMAPS; SCIENCE AND TECHNOLOGY; TECHNOLOGY INSERTION; TECHNOLOGY STRATEGY; TECHNOLOGY TRANSFER
#> 6 <NA>
#> 7 STRATEGIC MANAGEMENT RESEARCH; BIBLIOMETRICS; CO-CITATION ANALYSIS
#> 8 <NA>
#> 9 <NA>
#> 10 <NA>
#> 11 AUTHOR CO-CITATION ANALYSIS; PATHFINDER ANALYSIS; INFORMATION THEORY; STRATEGIC MANAGEMENT RESEARCH; BIBLIOMETRICS
#> 12 CITATION ANALYSIS; BIBLIOMETRIC TECHNIQUES; SCHOLARLY IMPACT; UNIVERSITY IMPACT
#> 13 EMERGING TECHNOLOGIES; FORECASTING; CITATION NETWORK; BIBLIOMETRICS; SUSTAINABLE ENERGY; RENEWABLE ENERGY
#> 14 ENTREPRENEURSHIP; RESEARCH FIELD; HANDBOOKS; BIBLIOMETRIC ANALYSIS
#> 15 INNOVATION STUDIES; NEW SCIENTIFIC FIELDS; SPECIALISMS; BIBLIOMETRICS; HANDBOOKS
#> 16 INNOVATION; MULTIDISCIPLINARITY; KNOWLEDGE CONVERGENCE; ABSORPTIVE CAPACITY; CREATIVE CAPACITY
#> 17 BIBLIOMETRICS; CO-CITATION; BIBLIOGRAPHIC COUPLING; SCIENCE MAPPING
#> 18 BUSINESS RESEARCH; BIBLIOMETRICS; WEB OF SCIENCE; JOURNAL ANALYSIS
#> 19 OPERATIONAL RESEARCH; MANAGEMENT SCIENCE; BIBLIOMETRICS; WEB OF SCIENCE; CITATION ANALYSIS
#> 20 BIBLIOMETRICS; SCIENCE MAPPING; BUSINESS MARKETING; JOURNAL OF BUSINESS & INDUSTRIAL MARKETING
#> KeywordsPlus
#> 1 <NA>
#> 2 SCIENTOMETRIC TRANSACTION MATRICES; CO-CITATION ANALYSIS; NETWORKS
#> 3 <NA>
#> 4 TECHNOLOGY; DIFFUSION
#> 5 TECHNICAL INTELLIGENCE; DATABASE TOMOGRAPHY
#> 6 UNIVERSITY-RESEARCH PERFORMANCE; CITATION ANALYSIS; BASIC RESEARCH; CO-CITATION; ECONOMICS; FLANDERS; POLICY; FIELD
#> 7 AUTHOR COCITATION ANALYSIS; DIVERSIFICATION STRATEGY; COMPETITIVE ADVANTAGE; LOCAL SEARCH; KNOWLEDGE; FIRM; PERFORMANCE; IMPACT; DISCIPLINE; BEHAVIOR
#> 8 <NA>
#> 9 VENTURE PERFORMANCE; KNOWLEDGE; ORGANIZATIONS; INNOVATION; CREATION; CONTEXT; ENTRY
#> 10 AUTHOR COCITATION ANALYSIS; LIMITATIONS; PARADIGMS; SYSTEMS; SCIENCE; POLICY; FIRMS
#> 11 CITATION ANALYSIS; JOURNALS; SYSTEMS; DISCIPLINE; SCIENCE
#> 12 RESEARCH PRODUCTIVITY; JOB-PERFORMANCE; JOURNALS; FACULTY; SCIENCE; MODEL; INFORMETRICS; METAANALYSIS; PERSONALITY; RECOGNITION
#> 13 HYDROGEN FUTURES; BIBLIOMETRICS; POLICY; SCIENCE; RENEWABLES; MECHANISMS; PRIORITIES
#> 14 SCIENCE POLICY; INNOVATION; ECONOMICS; GROWTH; SPILLOVERS; DISCOVERY; EMERGENCE; MARKET; FIELD
#> 15 SOCIAL-SCIENCE; ECONOMICS
#> 16 SCIENTIFIC LITERATURES; AUTHOR COCITATION; CITATION ANALYSIS; MANAGEMENT; TECHNOLOGY
#> 17 PRODUCT-INNOVATION-MANAGEMENT; EXPLORATORY FACTOR-ANALYSIS; AUTHOR COCITATION ANALYSIS; INTELLECTUAL STRUCTURE; STRATEGIC-MANAGEMENT; BUSINESS ETHICS; ENTREPRENEURSHIP-RESEARCH; OPERATIONS MANAGEMENT; ABSORPTIVE-CAPACITY; INFORMATION-SCIENCE
#> 18 RETROSPECTIVE EVALUATION; RESEARCH PRODUCTIVITY; ECONOMICS; AUTHORS; MANAGEMENT; ARTICLES; RANKINGS; DECADES
#> 19 MANAGEMENT SCIENCE; ECONOMICS
#> 20 PRODUCT; HISTORY; IMPACT
#> DOI Year LCS GCS
#> 1 10.1016/0048-7333(85)90012-5 1985 22 310
#> 2 10.1086/209319 1993 34 127
#> 3 10.1016/0040-1625(95)00022-3 1995 20 165
#> 4 10.1016/S0040-1625(97)00050-4 1997 18 168
#> 5 10.1109/17.922473 2001 19 387
#> 6 10.1111/1468-2370.00083 2002 18 117
#> 7 10.1002/smj.397 2004 108 667
#> 8 10.1016/j.techfore.2006.04.004 2006 51 569
#> 9 10.1111/j.1540-6520.2006.00126.x 2006 35 167
#> 10 10.1111/j.1741-6248.2007.00092.x 2007 21 101
#> 11 10.1002/smj.659 2008 67 353
#> 12 10.1177/0149206308319533 2008 32 271
#> 13 10.1016/j.techfore.2007.05.005 2008 25 160
#> 14 10.1016/j.respol.2012.03.009 2012 21 186
#> 15 10.1016/j.respol.2012.03.008 2012 22 174
#> 16 10.1002/smj.2002 2013 20 132
#> 17 10.1177/1094428114562629 2015 71 844
#> 18 10.1016/j.jbusres.2015.04.006 2015 36 179
#> 19 10.1016/j.ejor.2017.04.027 2017 22 165
#> 20 10.1108/JBIM-04-2016-0079 2017 25 118
Aria, M. & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007 (https://doi.org/10.1016/j.joi.2017.08.007)
Aria, M., Cuccurullo, C., D’Aniello, L., Misuraca, M., & Spano, M. (2022). Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in Italy. Sustainability, 14(6), 3643, (https://doi.org/10.3390/su14063643)
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Aria M., Alterisio A., Scandurra A, Pinelli C., D’Aniello B, (2021) The scholar’s best friend: research trends in dog cognitive and behavioural studies, Animal Cognition. (https://doi.org/10.1007/s10071-020-01448-2)
Belfiore, A., Cuccurullo, C., & Aria, M. (2022). IoT in healthcare: A scientometric analysis. Technological Forecasting and Social Change, 184, 122001. (https://doi.org/10.1016/j.techfore.2022.122001)
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