Bibliographic Collection
Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)
Data format: Plaintext
Query: SO = “People analytics or HR analytics”
Document Type: Articles, letters, review and
proceedings papers
Query data: Nov, 2022
Install and load bibliometrix R-package
# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line
# install.packages("bibliometrix")
# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
library(bibliometrix)
## 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.
##
##
## https://www.bibliometrix.org
##
##
## For information and bug reports:
## - Send an email to info@bibliometrix.org
## - Write a post on https://github.com/massimoaria/bibliometrix/issues
##
## Help us to keep Bibliometrix 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 shiny web-interface, please digit:
## biblioshiny()
Data Loading and Converting
myfile <- ("../data/data_search.ciw")
# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(file=myfile, dbsource="wos",format="plaintext")
##
## Converting your wos collection into a bibliographic dataframe
##
## Done!
##
##
## Generating affiliation field tag AU_UN from C1: Done!
Section 1: Descriptive Analysis
Although bibliometrics is mainly known for quantifying the scientific
production and measuring its quality and impact, it is also useful for
displaying and analysing the intellectual, conceptual and social
structures of research as well as their evolution and dynamical
aspects.
In this way, bibliometrics aims to describe how specific disciplines,
scientific domains, or research fields are structured and how they
evolve over time. In other words, bibliometric methods help to map the
science (so-called science mapping) and are very useful in the case of
research synthesis, especially for the systematic ones.
Bibliometrics is an academic science founded on a set of statistical
methods, which can be used to analyze scientific big data quantitatively
and their evolution over time and discover information. Network
structure is often used to model the interaction among authors,
papers/documents/articles, references, keywords, etc.
Bibliometrix is an open-source software for automating the stages of
data-analysis and data-visualization. After converting and uploading
bibliographic data in R, Bibliometrix performs a descriptive analysis
and different research-structure analysis.
Descriptive analysis provides some snapshots about the annual
research development, the top “k” productive authors, papers, countries
and most relevant keywords.
Main findings about the collection
#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)
MAIN INFORMATION ABOUT DATA
Timespan 1995 : 2023
Sources (Journals, Books, etc) 251
Documents 437
Annual Growth Rate % 0
Document Average Age 3.51
Average citations per doc 22.66
Average citations per year per doc 5.217
References 21912
DOCUMENT TYPES
article 368
article; early access 43
article; proceedings paper 6
correction 3
editorial material 4
review 12
review; early access 1
DOCUMENT CONTENTS
Keywords Plus (ID) 805
Author's Keywords (DE) 1439
AUTHORS
Authors 1199
Author Appearances 1366
Authors of single-authored docs 41
AUTHORS COLLABORATION
Single-authored docs 41
Documents per Author 0.364
Co-Authors per Doc 3.13
International co-authorships % 32.27
Annual Scientific Production
Year Articles
1995 1
1998 1
1999 2
2000 2
2001 2
2002 3
2003 1
2004 1
2005 2
2007 2
2008 2
2009 4
2010 1
2011 4
2012 4
2013 3
2014 8
2015 8
2016 11
2017 32
2018 35
2019 48
2020 60
2021 82
2022 73
2023 1
Annual Percentage Growth Rate 0
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 PANDA S 7 PANDA S 4.00
2 RATH SK 6 RATH SK 3.00
3 GONG YM 5 SURESH M 1.83
4 LIU HF 5 MIKALEF P 1.58
5 LIU S 5 VAN DE WETERING R 1.58
6 ZHANG JL 5 PINSONNEAULT A 1.50
7 MAO HY 4 ZHANG JL 1.48
8 MIKALEF P 4 AL-OMOUSH KS 1.33
9 SURESH M 4 GONG YM 1.23
10 TALLON PP 4 LIU S 1.23
Top manuscripts per citations
Paper DOI TC TCperYear NTC
1 ZHANG CY, 2019, IEEE COMMUN SURV TUT 10.1109/COMST.2019.2904897 559 139.8 17.91
2 TEECE D, 2016, CALIF MANAGE REV 10.1525/cmr.2016.58.4.13 532 76.0 5.52
3 LU Y, 2011, MIS QUART NA 491 40.9 2.04
4 TALLON PP, 2011, MIS QUART NA 446 37.2 1.85
5 VERHOEF PC, 2021, J BUS RES 10.1016/j.jbusres.2019.09.022 359 179.5 33.30
6 MEADE LM, 1999, INT J PROD RES 10.1080/002075499191751 351 14.6 1.68
7 CONBOY K, 2009, INFORM SYST RES 10.1287/isre.1090.0236 343 24.5 2.14
8 MIKALEF P, 2017, J BUS RES 10.1016/j.jbusres.2016.09.004 290 48.3 8.71
9 LAWLER JJ, 2009, ANN NY ACAD SCI 10.1111/j.1749-6632.2009.04147.x 215 15.4 1.34
10 LIU HF, 2016, J OPER MANAG 10.1016/j.jom.2016.03.009 201 28.7 2.08
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 USA 65 0.1508 49 16 0.246
2 CHINA 50 0.1160 26 24 0.480
3 IRAN 25 0.0580 23 2 0.080
4 INDIA 23 0.0534 21 2 0.087
5 GERMANY 21 0.0487 15 6 0.286
6 UNITED KINGDOM 19 0.0441 12 7 0.368
7 BRAZIL 16 0.0371 13 3 0.188
8 CANADA 16 0.0371 10 6 0.375
9 INDONESIA 13 0.0302 13 0 0.000
10 SPAIN 12 0.0278 8 4 0.333
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 USA 3610 55.54
2 CHINA 923 18.46
3 UNITED KINGDOM 895 47.11
4 NETHERLANDS 487 60.88
5 NORWAY 404 80.80
6 SPAIN 359 29.92
7 IRELAND 354 177.00
8 PORTUGAL 299 49.83
9 INDIA 277 12.04
10 CANADA 213 13.31
Most Relevant Sources
Sources Articles
1 SUSTAINABILITY 14
2 JOURNAL OF BUSINESS RESEARCH 11
3 TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 11
4 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 8
5 INDUSTRIAL MANAGEMENT & DATA SYSTEMS 8
6 INFORMATION & MANAGEMENT 8
7 INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 8
8 EUROPEAN JOURNAL OF INFORMATION SYSTEMS 7
9 INFORMATION SYSTEMS RESEARCH 7
10 JOURNAL OF STRATEGIC INFORMATION SYSTEMS 7
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 ORGANIZATIONAL AGILITY 116 ORGANIZATIONAL AGILITY 125
2 AGILITY 64 FIRM PERFORMANCE 104
3 AGILE MANAGEMENT 24 INFORMATION-TECHNOLOGY 93
4 DYNAMIC CAPABILITIES 22 PERFORMANCE 79
5 AGILE 16 DYNAMIC CAPABILITIES 73
6 FIRM PERFORMANCE 12 MANAGEMENT 62
7 ABSORPTIVE CAPACITY 11 IMPACT 56
8 DIGITAL TRANSFORMATION 11 COMPETITIVE ADVANTAGE 53
9 INNOVATION 11 INNOVATION 51
10 IT CAPABILITY 11 SYSTEMS 44
plot(x=results, k=10, pause=F)
Most Cited References
CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
[,1]
LU Y, 2011, MIS QUART, V35, P931 153
SAMBAMURTHY V, 2003, MIS QUART, V27, P237 134
TALLON PP, 2011, MIS QUART, V35, P463 128
OVERBY E, 2006, EUR J INFORM SYST, V15, P120, DOI 10.1057/PALGRAVE.EJIS.3000600 92
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 86
TEECE DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO 83
CHAKRAVARTY A, 2013, INFORM SYST RES, V24, P976, DOI 10.1287/ISRE.2013.0500 82
PODSAKOFF PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879 78
TEECE D, 2016, CALIF MANAGE REV, V58, P13, DOI 10.1525/CMR.2016.58.4.13 69
CHEN Y, 2014, EUR J INFORM SYST, V23, P326, DOI 10.1057/EJIS.2013.4 58
BHARADWAJ AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983 57
EISENHARDT KM, 2000, STRATEGIC MANAGE J, V21, P1105, DOI 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO 54
VAN OOSTERHOUT M, 2006, EUR J INFORM SYST, V15, P132, DOI 10.1057/PALGRAVE.EJIS.3000601 54
TEECE DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/SMJ.640 53
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108 52
ROBERTS N, 2012, J MANAGE INFORM SYST, V28, P231, DOI 10.2753/MIS0742-1222280409 51
RAVICHANDRAN T, 2018, J STRATEGIC INF SYST, V27, P22, DOI 10.1016/J.JSIS.2017.07.002 48
SHEREHIY B, 2007, INT J IND ERGONOM, V37, P445, DOI 10.1016/J.ERGON.2007.01.007 47
LEE OK, 2015, INFORM SYST RES, V26, P398, DOI 10.1287/ISRE.2015.0577 46
WADE M, 2004, MIS QUART, V28, P107 45
Section 2: The Intellectual Structure of the field - Co-citation
Analysis
Citation analysis is one of the main classic techniques in
bibliometrics. It shows the structure of a specific field through the
linkages between nodes (e.g. authors, papers, journal), while the edges
can be differently interpretated depending on the network type, that are
namely co-citation, direct citation, bibliographic coupling. Please see
Aria, Cuccurullo (2017).
Below there are three examples.
First, a co-citation network that shows relations between
cited-reference works (nodes).
Second, a co-citation network that uses cited-journals as unit of
analysis.
The useful dimensions to comment the co-citation networks are: (i)
centrality and peripherality of nodes, (ii) their proximity and
distance, (iii) strength of ties, (iv) clusters, (iiv) bridging
contributions.
Third, a historiograph is built on direct citations. It draws the
intellectual linkages in a historical order. Cited works of thousands of
authors contained in a collection of published scientific articles is
sufficient for recostructing the historiographic structure of the field,
calling out the basic works in it.
Article (References) co-citation analysis
Plot options:
n = 50 (the funxtion plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 1 (defines the size of vertex labels)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
edges.min = 5 (plots only edges with a strength greater than or
equal to 5)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)
Descriptive analysis of Article co-citation network
characteristics
#netstat <- networkStat(NetMatrix)
#summary(netstat,k=10)
Journal (Source) co-citation analysis
M=metaTagExtraction(M,"CR_SO",sep=";")
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "sources", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=15, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)
Descriptive analysis of Journal co-citation network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 8206
Density 0.016
Transitivity 0.254
Diameter 4
Degree Centralization 0.494
Average path length 2.258
Section 3: Historiograph - Direct citation linkages
histResults <- histNetwork(M, sep = ";")
##
## WOS DB:
## Searching local citations (LCS) by reference items (SR) and DOIs...
##
## Analyzing 32930 reference items...
##
## Found 167 documents with no empty Local Citations (LCS)
options(width = 130)
net <- histPlot(histResults, n=20, size = 5, labelsize = 4)
Legend
Label
1 BREU K, 2002, J INF TECHNOL-UK DOI 10.1080/02683960110132070
2 CROCITTO M, 2003, IND MANAGE DATA SYST DOI 10.1108/02635570310479963
3 ZAIN M, 2005, INFORM MANAGE-AMSTER DOI 10.1016/J.IM.2004.09.001
4 FINK L, 2007, J ASSOC INF SYST DOI 10.17705/1JAIS.00135
5 SEO D, 2008, COMMUN ACM DOI 10.1145/1400214.1400242
6 CONBOY K, 2009, INFORM SYST RES DOI 10.1287/ISRE.1090.0236
7 NIJSSEN M, 2012, INT J HUM RESOUR MAN DOI 10.1080/09585192.2012.689160
8 CHAKRAVARTY A, 2013, INFORM SYST RES DOI 10.1287/ISRE.2013.0500
9 LEE OK, 2015, INFORM SYST RES DOI 10.1287/ISRE.2015.0577
10 MAO HY, 2015, INFORM DEV DOI 10.1177/0266666913518059
11 CEGARRA-NAVARRO JG, 2016, J BUS RES DOI 10.1016/J.JBUSRES.2015.10.014
12 PANDA S, 2016, J ENTERP INF MANAG DOI 10.1108/JEIM-04-2015-0033
13 TEECE D, 2016, CALIF MANAGE REV DOI 10.1525/CMR.2016.58.4.13
14 FELIPE CM, 2016, J BUS RES DOI 10.1016/J.JBUSRES.2016.04.014
15 APPELBAUM SH, 2017, IND COMMER TRAIN DOI 10.1108/ICT-05-2016-0027
16 CORTE-REAL N, 2017, J BUS RES DOI 10.1016/J.JBUSRES.2016.08.011
17 PARK Y, 2017, J ASSOC INF SYST DOI 10.17705/1JAIS.00001
18 TAN FTC, 2017, INFORM MANAGE-AMSTER DOI 10.1016/J.IM.2016.08.001
19 RAVICHANDRAN T, 2018, J STRATEGIC INF SYST DOI 10.1016/J.JSIS.2017.07.002
20 LIU S, 2018, INT J INFORM MANAGE DOI 10.1016/J.IJINFOMGT.2018.07.010
21 CAI Z, 2019, R&D MANAGE DOI 10.1111/RADM.12305
22 ASHRAFI A, 2019, INT J INFORM MANAGE DOI 10.1016/J.IJINFOMGT.2018.12.005
23 TALLON PP, 2019, J STRATEGIC INF SYST DOI 10.1016/J.JSIS.2018.12.002
Author_Keywords
1 <NA>
2 ORGANIZATIONS; AGILE PRODUCTION; LEADERSHIP; HUMAN RESOURCE DEVELOPMENT
3 IT ACCEPTANCE; IT ADOPTION; ORGANIZATIONAL AGILITY; STRUCTURAL EQUATION MODELS; MALAYSIA
4 IT PERSONNEL CAPABILITIES; IT INFRASTRUCTURE CAPABILITIES; IT-DEPENDENT ORGANIZATIONAL AGILITY; IT-DEPENDENT STRATEGIC AGILITY
5 <NA>
6 AGILE; SYSTEMS DEVELOPMENT; CONCEPTUAL RESEARCH; AGILE MANUFACTURING; AGILE MANAGEMENT
7 AGILITY; DYNAMIC ENVIRONMENT; INSTITUTIONAL PRESSURE; STRATEGIC RESPONSE
8 ORGANIZATIONAL AGILITY; IT COMPETENCIES; LATENT CLASS REGRESSION
9 AGILITY; IT AMBIDEXTERITY; OPERATIONAL AMBIDEXTERITY; ENVIRONMENTAL DYNAMISM; MODERATED-MEDIATION ANALYSIS
10 INFORMATION TECHNOLOGY CAPABILITY; KNOWLEDGE CAPABILITY; ORGANIZATIONAL AGILITY; ENVIRONMENTAL UNCERTAINTY; INFORMATION INTENSITY; CHINA
11 KNOWLEDGE PROCESSES; ORGANIZATIONAL AGILITY; FIRM PERFORMANCE; KNOWLEDGE CONVERSION
12 IT CAPABILITY; ORGANIZATIONAL AGILITY; IT-AGILITY LINK; IT SPENDING; STRUCTURAL MODELLING; INTERACTION-MODERATION
13 COMPETITIVE ADVANTAGE; COMPETITIVEC STRATEGY; STRATEGIC MANAGEMENT
14 ORGANIZATIONAL AGILITY; INFORMATION SYSTEMS CAPABILITIES; ABSORPTIVE CAPACITY; HIERARCHY CULTURE; PARTIAL LEAST SQUARES (PLS); CONDITIONAL MEDIATION ANALYSIS
15 PERFORMANCE MANAGEMENT; AGILITY; ORGANIZATIONAL TRANSFORMATION; DYNAMIC CAPABILITIES
16 BIG DATA ANALYTICS (BDA); IT BUSINESS VALUE; KNOWLEDGE BASED VIEW (KBV); DYNAMIC CAPABILITIES (DC); ORGANIZATIONAL AGILITY; COMPETITIVE ADVANTAGE
17 SENSING AGILITY; DECISION MAKING AGILITY; ACTING AGILITY; BUSINESS INTELLIGENCE TECHNOLOGY; COMMUNICATION TECHNOLOGY; CONFIGURATIONAL PARADIGM; FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS (FSQCA)
18 OPERATIONAL AGILITY; RESOURCE INTERDEPENDENCIES; IT CAPABILITIES; ENTERPRISE SYSTEMS; CASE STUDY
19 IT STRATEGY; AGILITY; IT COMPETENCE; COMPLEMENTARITIES
20 CLOUD COMPUTING; ORGANIZATIONAL AGILITY; IT INFRASTRUCTURE CAPABILITIES; IT SPENDING
21 <NA>
22 BUSINESS ANALYTICS; AGILITY; INFORMATION QUALITY; INNOVATIVE CAPABILITY; ENVIRONMENTAL TURBULENCE; PARTIAL LEAST SQUARES
23 ORGANIZATIONAL AGILITY; DIGITAL OPTIONS; IT ADAPTIVENESS; IT FLEXIBILITY; RESPONSIVENESS; IT-ENABLED AGILITY
KeywordsPlus
1 SUPPLY CHAIN; KEY ISSUES; MANAGEMENT; ORGANIZATIONS; TECHNOLOGY; TIME; IMPLEMENTATION; COMMUNICATION; SYSTEMS
2 TOTAL QUALITY MANAGEMENT; SYSTEMS
3 PERCEIVED USEFULNESS; COMPUTER-TECHNOLOGY; USER ACCEPTANCE; EASE; USAGE; SATISFACTION; SUCCESS
4 INFORMATION-TECHNOLOGY INFRASTRUCTURE; EXPLORATORY ANALYSIS; JOB SKILLS; BUSINESS; SYSTEMS; FLEXIBILITY; EQUATION; WEB; PROFESSIONALS; KNOWLEDGE
5 INFORMATION-SYSTEMS
6 DEVELOPMENT METHODOLOGIES; SUPPLY CHAIN; ACHIEVING AGILITY; FLEXIBILITY; MANAGEMENT; DESIGN; FIELD; WORK; MIS
7 COMPETITIVE ADVANTAGE; KNOWLEDGE; ENVIRONMENTS; TESTS
8 SUSTAINED COMPETITIVE ADVANTAGE; DYNAMIC CAPABILITIES; MARKET ORIENTATION; E-COMMERCE; ENVIRONMENTAL DYNAMISM; BUSINESS VALUE; SYSTEMS; INTEGRATION; REGRESSION; OUTCOMES
9 INFORMATION-SYSTEMS RESEARCH; RESOURCE-BASED PERSPECTIVE; COMPETITIVE ADVANTAGE; FIRM PERFORMANCE; TECHNOLOGY CAPABILITY; EMPIRICAL-EXAMINATION; STRATEGY FORMULATION; DYNAMIC CAPABILITIES; INNOVATION; ENVIRONMENTS
10 RESOURCE-BASED VIEW; COMMON METHOD VARIANCE; FIRM PERFORMANCE; ENTERPRISE AGILITY; MANAGEMENT CAPABILITY; TECHNOLOGY CAPABILITY; SYSTEMS; PERSPECTIVE; ALIGNMENT; IMPACT
11 DYNAMIC CAPABILITIES; ABSORPTIVE-CAPACITY; MANAGEMENT; ENABLERS; LINK
12 INFORMATION-TECHNOLOGY CAPABILITY; FIRM PERFORMANCE; BUSINESS VALUE; SYSTEMS; INFRASTRUCTURE; COVARIANCE; ROLES
13 PUNCTUATED EQUILIBRIUM; ADAPTABILITY
14 INFORMATION-TECHNOLOGY CAPABILITY; ABSORPTIVE-CAPACITY; FIRM PERFORMANCE; ENTERPRISE AGILITY; PERSPECTIVE; ALIGNMENT; ROLES; LINK
15 PUNCTUATED EQUILIBRIUM; TRANSFORMATION; PERFORMANCE; DYNAMICS; FIRMS; MODEL
16 INFORMATION-TECHNOLOGY CAPABILITY; RESOURCE-BASED VIEW; DYNAMIC CAPABILITIES; KNOWLEDGE MANAGEMENT; ORGANIZATIONAL PERFORMANCE; COMPETITIVE ADVANTAGE; STRATEGIC MANAGEMENT; DECISION-MAKING; RESEARCH AGENDA; ENVIRONMENTS
17 INFORMATION-TECHNOLOGY; TURBULENT ENVIRONMENTS; COMPETITIVE ADVANTAGE; FIRM SIZE; CAPABILITIES; PERFORMANCE; SYSTEMS; GOVERNANCE; MANAGEMENT; STRATEGY
18 INFORMATION-TECHNOLOGY; ORGANIZATIONAL AGILITY; ERP IMPLEMENTATION; ENTERPRISE SYSTEMS; BUSINESS MANAGERS; PERFORMANCE; COEVOLUTION; PERCEPTIONS; INTEGRATION; CAPABILITY
19 STRATEGIC INFORMATION-SYSTEMS; DYNAMIC CAPABILITIES; COMPETITIVE ADVANTAGE; TECHNOLOGY INVESTMENT; OPERATIONAL AGILITY; FIRM PERFORMANCE; VALUE CREATION; E-BUSINESS; INTEGRATION; SUCCESS
20 INFORMATION-TECHNOLOGY CAPABILITY; SUPPLY CHAIN INTEGRATION; FIRM PERFORMANCE; SERVICE PROVIDERS; SOCIAL COMMERCE; ADOPTION; SYSTEMS; INFRASTRUCTURE; IMPACT; PERSPECTIVE
21 KNOWLEDGE MANAGEMENT CAPABILITY; INFORMATION-TECHNOLOGY CAPABILITY; TRANSACTIVE MEMORY-SYSTEMS; SUPPLY CHAIN INTEGRATION; FIRM PERFORMANCE; DYNAMIC CAPABILITIES; ENTERPRISE AGILITY; COMPETENCE; IMPACT; ORIENTATION
22 BIG DATA ANALYTICS; SUPPLY CHAIN AGILITY; INFORMATION-TECHNOLOGY CAPABILITY; INTELLIGENCE SYSTEMS; ORGANIZATIONAL AGILITY; DYNAMIC CAPABILITIES; ABSORPTIVE-CAPACITY; MARKET ORIENTATION; INNOVATION CAPABILITY; COMPETITIVE ADVANTAGE
23 BUSINESS PROCESS; OPERATIONAL AGILITY; ENTERPRISE AGILITY; CUSTOMER AGILITY; SERVICE QUALITY; MEDIATING ROLE; PERFORMANCE; CAPABILITY; STRATEGY; MANAGEMENT
DOI Year LCS GCS
1 10.1080/02683960110132070 2002 20 107
2 10.1108/02635570310479963 2003 30 96
3 10.1016/j.im.2004.09.001 2005 22 89
4 10.17705/1jais.00135 2007 21 119
5 10.1145/1400214.1400242 2008 15 50
6 10.1287/isre.1090.0236 2009 18 343
7 10.1080/09585192.2012.689160 2012 16 74
8 10.1287/isre.2013.0500 2013 82 196
9 10.1287/isre.2015.0577 2015 46 153
10 10.1177/0266666913518059 2015 21 44
11 10.1016/j.jbusres.2015.10.014 2016 37 117
12 10.1108/JEIM-04-2015-0033 2016 16 23
13 10.1525/cmr.2016.58.4.13 2016 69 532
14 10.1016/j.jbusres.2016.04.014 2016 31 91
15 10.1108/ICT-05-2016-0027 2017 15 31
16 10.1016/j.jbusres.2016.08.011 2017 17 189
17 10.17705/1jais.00001 2017 31 94
18 10.1016/j.im.2016.08.001 2017 15 47
19 10.1016/j.jsis.2017.07.002 2018 48 140
20 10.1016/j.ijinfomgt.2018.07.010 2018 15 41
21 10.1111/radm.12305 2019 15 31
22 10.1016/j.ijinfomgt.2018.12.005 2019 15 105
23 10.1016/j.jsis.2018.12.002 2019 35 84
Section 4: The conceptual structure - Co-Word Analysis
Co-word networks show the conceptual structure, that uncovers links
between concepts through term co-occurences.
Conceptual structure is often used to understand the topics covered
by scholars (so-called research front) and identify what are the most
important and the most recent issues.
Dividing the whole timespan in different timeslices and comparing the
conceptual structures is useful to analyze the evolution of topics over
time.
Bibliometrix is able to analyze keywords, but also the terms in the
articles’ titles and abstracts. It does it using network analysis or
correspondance analysis (CA) or multiple correspondance analysis (MCA).
CA and MCA visualise the conceptual structure in a two-dimensional
plot.
Co-word Analysis through Keyword co-occurrences
Plot options:
normalize = “association” (the vertex similarities are normalized
using association strength)
n = 50 (the function plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of the vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 3 (defines the max size of vertex labels)
label.cex = TRUE (The vertex label sizes are proportional to
their degree)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
label.n = 30 (Labels are plotted only for the main 30
vertices)
edges.min = 25 (plots only edges with a strength greater than or
equal to 2)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=5,label.cex=TRUE,label.n=30,edges.min=2)
Descriptive analysis of keyword co-occurrences network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 805
Density 0.024
Transitivity 0.216
Diameter 5
Degree Centralization 0.434
Average path length 2.45
Section 5: Thematic Map
Co-word analysis draws clusters of keywords. They are considered as
themes, whose density and centrality can be used in classifying themes
and mapping in a two-dimensional diagram.
Thematic map is a very intuitive plot and we can analyze themes
according to the quadrant in which they are placed: (1) upper-right
quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3)
lower-left quadrant: emerging or disappearing themes; (4) upper-left
quadrant: very specialized/niche themes.
Please see:
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).
Aria M., Misuraca M., Spano M. (2020) Mapping the evolution
of social research and data science on 30 years of Social Indicators
Research, Social Indicators Research. (DOI: )https://doi.org/10.1007/s11205-020-02281-3)
Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera,
F. (2011). An approach for detecting, quantifying, and
visualizing the evolution of a research field: A practical application
to the fuzzy sets theory field. Journal of
Informetrics, 5(1), 146-166.
Map=thematicMap(M, field = "ID", n = 250, minfreq = 4,
stemming = FALSE, size = 0.7, n.labels=5, repel = TRUE)
plot(Map$map)
Cluster description
Clusters=Map$words[order(Map$words$Cluster,-Map$words$Occurrences),]
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
CL <- Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
## # A tibble: 41 × 9
## # Groups: Cluster_Label [9]
## Occurrences Words Cluster Color Cluster_Label Cluster_Frequency btw_centra…¹ clos_…² pager…³
## <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 93 information-technology 1 #E41A1C80 information-technology 1310 787. 0.00221 0.0357
## 2 79 performance 1 #E41A1C80 information-technology 1310 1394. 0.00229 0.0282
## 3 62 management 1 #E41A1C80 information-technology 1310 1034. 0.00223 0.0223
## 4 56 impact 1 #E41A1C80 information-technology 1310 987. 0.00227 0.0223
## 5 51 innovation 1 #E41A1C80 information-technology 1310 1074. 0.00229 0.0199
## 6 4 satisfaction 2 #377EB880 satisfaction 17 32.0 0.00190 0.00150
## 7 4 user acceptance 2 #377EB880 satisfaction 17 10.9 0.00172 0.00157
## 8 3 innovation capability 2 #377EB880 satisfaction 17 37.0 0.00193 0.00192
## 9 2 information-systems success 2 #377EB880 satisfaction 17 15.2 0.00185 0.00135
## 10 2 media 2 #377EB880 satisfaction 17 11.4 0.00173 0.00123
## # … with 31 more rows, and abbreviated variable names ¹btw_centrality, ²clos_centrality, ³pagerank_centrality
Section 6: The social structure - Collaboration Analysis
Collaboration networks show how authors, institutions
(e.g. universities or departments) and countries relate to others in a
specific field of research. For example, the first figure below is a
co-author network. It discovers regular study groups, hidden groups of
scholars, and pivotal authors. The second figure is called “Edu
collaboration network” and uncovers relevant institutions in a specific
research field and their relations.
Author collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Author collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=1)
Descriptive analysis of author collaboration network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 1199
Density 0.003
Transitivity 0.923
Diameter 4
Degree Centralization 0.012
Average path length 1.411
Edu collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "universities", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Edu collaboration",type = "auto", size=4,size.cex=F,edgesize = 3,labelsize=1)
Descriptive analysis of edu collaboration network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 669
Density 0.004
Transitivity 0.723
Diameter 9
Degree Centralization 0.02
Average path length 4.014
Country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country collaboration",type = "circle", size=10,size.cex=T,edgesize = 1,labelsize=0.6, cluster="none")
Descriptive analysis of country collaboration network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 66
Density 0.086
Transitivity 0.395
Diameter 6
Degree Centralization 0.283
Average path length 2.538
