##. load the bibliometrix libary by Aria and Cuccurullo (2017)
library(bibliometrix)
##. Load mulitply files
a First load the folder where you have stored the Scopus cvs files.
It is good idea to save the files to my documents of to folder at root
(c:_folder), as it will be easier to navigate to the window with files.
(remeber the files for the tranings were stored here: https://www.dropbox.com/scl/fo/wfj5y836jmy64c57y9ch3/h?rlkey=5b3z0heidxg4p7d7orajc5ort&dl=0)
We use the function path <- choose.dir()
path <- choose.dir()
Secondly we save the list of all files in the directory
path
into objec c named files
files <- list.files(path = path, pattern = ".csv")
# shwo the files
files
[1] "Auditing_All_729.csv" "BRA_All_196.csv"
let us check our current working directory
getwd()
[1] "C:/Users/pstasz/Documents/R/AAW_2023_Private/0_session"
let us change the working directory to this we have the stored csv files:
setwd(path)
and let us check where we are now
getwd()
Convert the rough data into biliometrix object (it will take a
while). The objec we will name the All
setwd(path)
Ostrzeżenie: The working directory was changed to C:/Users/pstasz/Documents/R/AAW_2023_Private/Dane inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
paste0("This is a temporaly working directory for the importing of the files")
[1] "This is a temporaly working directory for the importing of the files"
getwd()
[1] "C:/Users/pstasz/Documents/R/AAW_2023_Private/Dane"
All<- convert2df(file = files, dbsource = "scopus", format = "csv")
Converting your scopus collection into a bibliographic dataframe
Done!
Generating affiliation field tag AU_UN from C1: Done!
# Check the working directory
getwd()
[1] "C:/Users/pstasz/Documents/R/AAW_2023_Private/0_session"
paste("Actually we come back to the orginal project directory")
[1] "Actually we come back to the orginal project directory"
Now we have the ablity to upload as many files as we wish combined into one dataset in our case called ‘All’
For details see: https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html
##. The improted data are taged
FN.File.Name |
---|
VR;Version Number |
PT;Publication Type (J=Joumal; B=Book; S=Series; P=Patent) |
AU;Authors |
AF;Author Full Name |
BA;Book Authors |
BF;Book Authors Full Name |
CA;Group Authors |
GP;Book Group Authors |
BE;Editors |
Tl;Document Title |
SO;Publication Name |
SE;Book Series Title |
BS;Book Series Subtitle |
LA;Language |
DT;Document Type |
CT;Conference Title |
CY;Conference Date |
CL;Conference Location |
SP;Conference Sponsors |
HO;Conference Host |
DE;Author Keywords |
ID;Keywords Plus® |
AB;Abstract |
C1;Author Address |
RP;Reprint Address |
EM;E-mail Address |
Rl;ResearcherlD Number |
Ol;ORCID Identifier (Open Researcher and Contributor ID) |
FU;Funding Agency and Grant Number |
FX;Funding Text |
CR;Cited References |
NR;Cited Reference Count |
TC;Web of Science Core Collection Times Cited Count |
Z9;Total Times Cited Count (Web of Science Core ColleUiuit |
BI03I3 Citatiun Index. Chinese 3cienie Citation Database. Data Citation Index. Russian Science Citation Index. SciELO Citation Index) |
U1;Usage Count (Last 180 Days) |
U2;Usage Count (Since 2013) |
PU;Publisher |
PI;Publisher City |
PA;Publisher Address |
SN;International Standard Serial Number (ISSN) |
El;Electronic International Standard Serial Number (elSSN) |
BN;International Standard Book Number (ISBN) |
J9;29-Character Source Abbreviation |
Jl;ISO Source Abbreviation |
PD;Publication Date |
PY;Year Published |
VL;Volume |
IS;Issue |
SI;Special Issue |
PN;Part Number |
SU;Supplement |
MA;Meeting Abstract |
BP;Beginning Page |
EP;Ending Page |
AR;Article Number |
Dl;Digital Object Identifier (DOI) |
D2;Book Digital Object Identifier (DOI) |
EA;Early access date |
EY;Early access year |
PG;Page Count |
P2;Chapter Count (Book Citation Index) |
WC;Web of Science Categories |
sc;Research Areas |
GA;Document Delivery Number |
PM;PubMed ID |
UT;Accession Number |
OA;Open Access Indicator |
HP;ESI Hot Paper. Note that this field is valued only for ESI subscribers. |
HC;ESI Highly Cited Paper. Note that this field is valued only for ESI subscribers. |
OA;Date this report was generated. |
ER;End of Record |
EF;End of File |
Those above are all avaialbe tags from Web os Science let us see how many tags we have imporeted with our data (recall our data we named All)
names(All)
[1] "AU" "Author.s..ID"
[3] "TI" "PY"
[5] "SO" "VL"
[7] "IS" "Art..No."
[9] "Page.start" "Page.end"
[11] "PP" "TC"
[13] "DI" "URL"
[15] "Affiliations" "C1"
[17] "AB" "DE"
[19] "ID" "Molecular.Sequence.Numbers"
[21] "Chemicals.CAS" "Tradenames"
[23] "Manufacturers" "FU"
[25] "FX" "Funding.Text.2"
[27] "Funding.Text.3" "Funding.Text.4"
[29] "Funding.Text.5" "Funding.Text.6"
[31] "Funding.Text.7" "Funding.Text.8"
[33] "Funding.Text.9" "Funding.Text.10"
[35] "CR" "RP"
[37] "Editors" "Sponsors"
[39] "PU" "Conference.name"
[41] "Conference.date" "Conference.location"
[43] "Conference.code" "ISSN"
[45] "ISBN" "CODEN"
[47] "PubMed.ID" "LA"
[49] "JI" "DT"
[51] "Publication.Stage" "OA"
[53] "DB" "UT"
[55] "J9" "AU_UN"
[57] "AU1_UN" "AU_UN_NR"
[59] "SR_FULL" "SR"
results<- biblioAnalysis(All, sep =";")
Let us see the results in viewer View(results)
Let us summarize the data (only first 10 items, means k=10)
options(width=100)
S <- summary(object = results, k = 10, pause = FALSE)
MAIN INFORMATION ABOUT DATA
Timespan 1996 : 2022
Sources (Journals, Books, etc) 2
Documents 925
Annual Growth Rate % -4.14
Document Average Age 11.6
Average citations per doc 36.7
Average citations per year per doc 2.66
References 42573
DOCUMENT TYPES
article 851
conference paper 16
editorial 15
erratum 7
note 6
review 30
DOCUMENT CONTENTS
Keywords Plus (ID) 0
Author's Keywords (DE) 2133
AUTHORS
Authors 1303
Author Appearances 2389
Authors of single-authored docs 112
AUTHORS COLLABORATION
Single-authored docs 132
Documents per Author 0.71
Co-Authors per Doc 2.58
International co-authorships % 18.16
Annual Scientific Production
Annual Percentage Growth Rate -4.14
Most Productive Authors
Top manuscripts per citations
Corresponding Author's Countries
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Most Relevant Sources
Most Relevant Keywords
NA
Basic plots
plot(x = results, k = 10, pause = FALSE)
Most cited (5 papers) [ it will take a while as we have 925 papers]
MCP <- citations(All, field = "article", sep = ";")
cbind(MCP$Cited[1:5])
[,1]
BUCKLESS, F.A., RAVENSCROFT, S.P., CONTRAST CODING: A REFINEMENT OF ANOVA IN BEHAVIORAL ANALYSIS (1990) THE ACCOUNTING REVIEW, 65 (4), PP. 933-945 25
CAREY, P., SIMNETT, R., AUDIT PARTNER TENURE AND AUDIT QUALITY (2006) THE ACCOUNTING REVIEW, 81 (3), PP. 653-676 22
SIMUNIC, D.A., THE PRICING OF AUDIT SERVICES: THEORY AND EVIDENCE (1980) JOURNAL OF ACCOUNTING RESEARCH, 18 (1), PP. 161-190 22
BECKER, C.L., DEFOND, M.L., JIAMBALVO, J., SUBRAMANYAM, K.R., THE EFFECT OF AUDIT QUALITY ON EARNINGS MANAGEMENT (1998) CONTEMPORARY ACCOUNTING RESEARCH, 15 (1), PP. 1-24 20
HAY, D.C., KNECHEL, W.R., WONG, N., AUDIT FEES: A META-ANALYSIS OF THE EFFECT OF SUPPLY AND DEMAND ATTRIBUTES (2006) CONTEMPORARY ACCOUNTING RESEARCH, 23 (1), PP. 141-191 18
We might as well check the the most frequent cited first author
citations(All, filed ="author", sep =";")
or most frequent
cited local autor (how many time author included in collection has been
cited in this collection)
LC<-localCitations(All, sep = ";")
we can access the
resuts in ‘View()’ or just by listing the specific fiels e.g
LC$Authors[1:5]
or LC$Papers[1:8]
, byw [1:n]
shows first \(n\) items.
The function AuthorProdOverTime
calculates and plots the
authors’ production (in terms of number of publications, and total
citations per year) over the time.
Function arguments are: All
a bibliographic data frame;
\(k\) is the number of \(k\) Top Authors; graph is a logical. If
graph=TRUE, the function plots the author production over time
graph.
topAU <- authorProdOverTime(All, k = 10, graph = TRUE)
Lotka’s law says that the number of authors publishing a certain number of articles is a fixed ratio to the number of authors publishing a single article. This means that there are many more authors who publish only one article than there are authors who publish two articles, and so on. (the theoretical coefficient is 2)
lotka <- lotka(results)
str(lotka)
List of 6
$ Beta : num 2.35
$ C : num 0.732
$ R2 : num 0.96
$ fitted : Named num [1:19] 0.7317 0.1433 0.0552 0.0281 0.0166 ...
..- attr(*, "names")= chr [1:19] "1" "2" "3" "4" ...
$ p.value : num 0.152
$ AuthorProd:'data.frame': 19 obs. of 3 variables:
..$ N.Articles: int [1:19] 1 2 3 4 5 6 7 8 9 10 ...
..$ N.Authors : num [1:19] 875 208 93 48 28 14 8 6 4 6 ...
..$ Freq : num [1:19] 0.6715 0.1596 0.0714 0.0368 0.0215 ...
Let us see the authors productivity
lotka$AuthorProd
A historiographic map is a graph that shows how scholarly articles are connected to each other over time.
histResults <- histNetwork(All, min.citations = 1, sep = ";")
SCOPUS DB: Searching local citations (LCS) by document titles (TI) and DOIs...
Found 625 documents with no empty Local Citations (LCS)
net <- histPlot(histResults, n=15, size = 10, labelsize=5)
Legend
Visualize the main items of three fields (e.g. authors, keywords, journals), and how they are related through a Sankey diagram.
threeFieldsPlot(All, fields = c("AU", "DE", "SO"), n = c(20, 20, 20))
Note for the standard references in examples at Bibliometrix they are usin gthe ID as the key words associated by the Scopus of Web of Science, in our data the ID is empty thus we replace it with author words, namely DE
thematicEvolution(
All)
Map=thematicMap(All, field = "DE", n = 50, minfreq = 4,
Warning messages:
1: ggrepel: 62 unlabeled data points (too many overlaps). Consider increasing max.overlaps
2: ggrepel: 62 unlabeled data points (too many overlaps). Consider increasing max.overlaps
stemming = FALSE, size = 0.7, n.labels=5, repel = TRUE)
Map
$map
$clusters
$words
$nclust
[1] 10
$net
$net$graph
IGRAPH 1b07c91 UNW- 50 220 --
+ attr: alpha (g/n), ylim (g/n), xlim (g/n), rescale
| (g/l), asp (g/n), layout (g/n), main (g/c), name (v/c),
| deg (v/n), size (v/n), label.cex (v/n), color (v/c),
| community (v/n), labelsize (v/n), label.dist (v/n),
| frame.color (v/c), label.color (v/c), label.font (v/n),
| label (v/c), weight (e/n), num (e/n), width (e/n),
| color (e/c), lty (e/n), curved (e/l)
+ edges from 1b07c91 (vertex names):
[1] audit quality--audit fees
[2] audit quality--auditing
+ ... omitted several edges
$net$graph_pajek
IGRAPH 1b0756d UNW- 50 220 --
+ attr: name (v/c), deg (v/n), size (v/n), label.cex
| (v/n), id (v/c), weight (e/n), num (e/n)
+ edges from 1b0756d (vertex names):
[1] audit quality--audit fees
[2] audit quality--auditing
[3] audit quality--corporate governance
[4] audit quality--auditor independence
[5] audit quality--audit committee
[6] audit quality--fraud
[7] audit quality--earnings management
+ ... omitted several edges
$net$cluster_obj
IGRAPH clustering walktrap, groups: 10, mod: 0.25
+ groups:
$`1`
[1] "auditor judgment" "professional skepticism"
[3] "auditor" "audit"
$`2`
[1] "auditor industry specialization"
[2] "auditor tenure"
[3] "going-concern"
$`3`
+ ... omitted several groups/vertices
$net$cluster_res
$net$community_obj
IGRAPH clustering walktrap, groups: 10, mod: 0.25
+ groups:
$`1`
[1] "auditor judgment" "professional skepticism"
[3] "auditor" "audit"
$`2`
[1] "auditor industry specialization"
[2] "auditor tenure"
[3] "going-concern"
$`3`
+ ... omitted several groups/vertices
$net$layout
[,1] [,2]
[1,] 0.312642636 0.280228625
[2,] 0.390858227 0.188962042
[3,] -0.079713073 0.415470169
[4,] 0.007992176 0.175301620
[5,] 0.381085891 0.072600697
[6,] -0.495372293 0.035851349
[7,] 0.223893047 0.585570848
[8,] -0.010876890 0.103923710
[9,] -0.171018834 0.291303709
[10,] 0.335617614 0.148343980
[11,] 0.365960392 0.641657336
[12,] 0.033964073 0.048894845
[13,] 0.008238903 -0.132509132
[14,] -0.154079991 0.403205562
[15,] -0.406354637 0.643308817
[16,] 0.452275660 0.185799738
[17,] 0.452723350 0.298218941
[18,] 0.347558359 0.918512257
[19,] 0.404223672 0.318444888
[20,] -0.474276990 0.113879388
[21,] 0.073896899 -0.124078863
[22,] -0.076327164 -0.063236400
[23,] 0.276972371 0.768872480
[24,] 0.271918035 0.232933755
[25,] 0.588034131 0.184248966
[26,] 0.427077347 0.405486589
[27,] 0.471679276 0.774260900
[28,] 0.110332546 0.004176688
[29,] 0.302898996 0.052829883
[30,] -0.204637789 0.535719191
[31,] 0.358992666 0.787952723
[32,] -0.514904483 0.546952457
[33,] 0.612192660 0.323795009
[34,] -0.361800908 0.335349141
[35,] 0.535999684 -0.469441018
[36,] 0.199976828 0.760228895
[37,] 0.047748094 0.347210644
[38,] 0.482128799 -0.007694795
[39,] -0.428568628 0.476157145
[40,] 0.202697401 0.409740620
[41,] -1.000000000 -1.000000000
[42,] -0.124664707 -0.013088682
[43,] -0.508646110 -0.421133246
[44,] -0.464745200 -0.149385276
[45,] 1.000000000 0.409201837
[46,] 0.324351205 -0.349572774
[47,] 0.854763177 0.508834749
[48,] 0.680354047 -0.627372575
[49,] -0.572000647 0.211851446
[50,] 0.256286185 1.000000000
$net$S
50 x 50 sparse Matrix of class "dsCMatrix"
audit quality 0.0090090090 0.0012718601
audit fees 0.0012718601 0.0117647059
auditing 0.0002434867 0.0009538951
corporate governance 0.0005460005 .
auditor independence 0.0006213110 0.0016227181
analytical procedures . .
audit committee 0.0007833921 0.0005115090
audit risk . 0.0039215686
fraud 0.0004504505 .
earnings management 0.0020020020 0.0019607843
internal control 0.0010598834 .
audit pricing 0.0011261261 0.0051470588
materiality 0.0005630631 .
audit planning . .
auditor judgment 0.0012012012 .
discretionary accruals 0.0048048048 0.0039215686
financial reporting quality 0.0038610039 0.0016806723
assurance 0.0006930007 .
restatements 0.0020790021 0.0036199095
audit quality 0.0002434867 0.0005460005 0.000621311
audit fees 0.0009538951 . 0.001622718
auditing 0.0270270270 0.0008190008 .
corporate governance 0.0008190008 0.0303030303 0.001044932
auditor independence . 0.0010449321 0.034482759
analytical procedures 0.0011750881 . .
audit committee . 0.0065876153 0.001499250
audit risk 0.0025740026 0.0014430014 .
fraud 0.0013513514 0.0015151515 .
earnings management 0.0015015015 . 0.005747126
internal control . . .
audit pricing . 0.0018939394 .
materiality . . .
audit planning 0.0036036036 0.0020202020 0.002298851
auditor judgment . . .
discretionary accruals . 0.0020202020 0.004597701
financial reporting quality . . .
assurance 0.0041580042 . .
restatements 0.0020790021 . .
audit quality . 0.0007833921 .
audit fees . 0.0005115090 0.003921569
auditing 0.001175088 . 0.002574003
corporate governance . 0.0065876153 0.001443001
auditor independence . 0.0014992504 .
analytical procedures 0.043478261 . .
audit committee . 0.0434782609 .
audit risk . . 0.047619048
fraud 0.002173913 0.0021739130 0.002380952
earnings management . 0.0024154589 0.002645503
internal control . . .
audit pricing . . 0.005952381
materiality . . 0.002976190
audit planning 0.002898551 . 0.003174603
auditor judgment . . 0.003174603
discretionary accruals . . 0.003174603
financial reporting quality . 0.0031055901 .
assurance . . .
restatements . 0.0033444816 0.003663004
audit quality 0.0004504505 0.002002002 0.001059883
audit fees . 0.001960784 .
auditing 0.0013513514 0.001501502 .
corporate governance 0.0015151515 . .
auditor independence . 0.005747126 .
analytical procedures 0.0021739130 . .
audit committee 0.0021739130 0.002415459 .
audit risk 0.0023809524 0.002645503 .
fraud 0.0500000000 . .
earnings management . 0.055555556 0.003267974
internal control . 0.003267974 0.058823529
audit pricing . 0.003472222 0.003676471
materiality 0.0031250000 0.003472222 .
audit planning 0.0066666667 . 0.003921569
auditor judgment . . .
discretionary accruals . 0.014814815 .
financial reporting quality . 0.003968254 .
assurance . . .
restatements . 0.004273504 .
audit quality 0.001126126 0.0005630631 .
audit fees 0.005147059 . .
auditing . . 0.003603604
corporate governance 0.001893939 . 0.002020202
auditor independence . . 0.002298851
analytical procedures . . 0.002898551
audit committee . . .
audit risk 0.005952381 0.0029761905 0.003174603
fraud . 0.0031250000 0.006666667
earnings management 0.003472222 0.0034722222 .
internal control 0.003676471 . 0.003921569
audit pricing 0.062500000 . 0.004166667
materiality . 0.0625000000 .
audit planning 0.004166667 . 0.066666667
auditor judgment . . .
discretionary accruals . . .
financial reporting quality . . .
assurance . . .
restatements . . .
audit quality 0.001201201 0.004804805 0.003861004
audit fees . 0.003921569 0.001680672
auditing . . .
corporate governance . 0.002020202 .
auditor independence . 0.004597701 .
analytical procedures . . .
audit committee . . 0.003105590
audit risk 0.003174603 0.003174603 .
fraud . . .
earnings management . 0.014814815 0.003968254
internal control . . .
audit pricing . . .
materiality . . .
audit planning . . .
auditor judgment 0.066666667 . .
discretionary accruals . 0.066666667 0.014285714
financial reporting quality . 0.014285714 0.071428571
assurance . . .
restatements . 0.005128205 0.005494505
audit quality 0.0006930007 0.002079002 .
audit fees . 0.003619910 .
auditing 0.0041580042 0.002079002 0.002079002
corporate governance . . 0.002331002
auditor independence . . .
analytical procedures . . 0.006688963
audit committee . 0.003344482 .
audit risk . 0.003663004 .
fraud . . 0.003846154
earnings management . 0.004273504 .
internal control . . .
audit pricing . . .
materiality . . .
audit planning . . 0.010256410
auditor judgment . . .
discretionary accruals . 0.005128205 .
financial reporting quality . 0.005494505 .
assurance 0.0769230769 . .
restatements . 0.076923077 .
audit quality 0.0015015015 0.0007507508 .
audit fees 0.0009803922 0.0009803922 .
auditing . 0.0022522523 .
corporate governance 0.0101010101 . 0.005050505
auditor independence . . 0.002873563
analytical procedures . 0.0036231884 .
audit committee . . 0.003623188
audit risk . 0.0079365079 .
fraud . 0.0083333333 .
earnings management . . .
internal control . . 0.004901961
audit pricing . 0.0052083333 .
materiality 0.0156250000 0.0156250000 .
audit planning . . 0.005555556
auditor judgment . . .
discretionary accruals . . .
financial reporting quality 0.0059523810 . .
assurance . . .
restatements . . .
audit quality 0.0045045045 0.001638002 0.001638002
audit fees 0.0009803922 0.005347594 0.002139037
auditing . . 0.002457002
corporate governance . . .
auditor independence 0.0028735632 . .
analytical procedures . . .
audit committee . . 0.003952569
audit risk . . .
fraud 0.0041666667 . .
earnings management . . .
internal control . . .
audit pricing . . .
materiality . . .
audit planning . . .
auditor judgment . . .
discretionary accruals . 0.006060606 0.006060606
financial reporting quality . . 0.006493506
assurance . . 0.006993007
restatements 0.0064102564 . 0.020979021
audit quality ......
audit fees ......
auditing ......
corporate governance ......
auditor independence ......
analytical procedures ......
audit committee ......
audit risk ......
fraud ......
earnings management ......
internal control ......
audit pricing ......
materiality ......
audit planning ......
auditor judgment ......
discretionary accruals ......
financial reporting quality ......
assurance ......
restatements ......
..............................
........suppressing 24 columns and 12 rows in show(); maybe adjust 'options(max.print= *, width = *)'
..............................
professional skepticism . .
audit report lag . 0.003921569
auditor . 0.001307190
auditor industry specialization 0.002002002 0.001307190
independence 0.001001001 .
litigation risk 0.001001001 0.001307190
nonaudit services . 0.001307190
audit . .
audit effort 0.002252252 .
audit judgment . .
business risk . 0.007352941
experience . .
fraudulent financial reporting . .
assurance services . 0.001680672
auditor tenure 0.001287001 0.001680672
corporate social responsibility 0.001287001 .
going-concern . .
hypothesis generation . .
outsourcing 0.002574003 .
professional skepticism 0.002702703 .
audit report lag . .
auditor . .
auditor industry specialization . .
independence 0.003003003 0.003367003
litigation risk . 0.006734007
nonaudit services . .
audit . 0.003787879
audit effort . .
audit judgment . .
business risk . .
experience . 0.003787879
fraudulent financial reporting . .
assurance services . .
auditor tenure . .
corporate social responsibility . .
going-concern . .
hypothesis generation 0.003861004 .
outsourcing . .
professional skepticism . .
audit report lag . .
auditor . .
auditor industry specialization . .
independence . .
litigation risk . .
nonaudit services 0.015325670 .
audit . 0.005434783
audit effort . .
audit judgment . .
business risk . 0.005434783
experience . .
fraudulent financial reporting . 0.005434783
assurance services . .
auditor tenure 0.004926108 .
corporate social responsibility . .
going-concern 0.004926108 .
hypothesis generation . 0.024844720
outsourcing . .
professional skepticism . .
audit report lag . .
auditor . .
auditor industry specialization . .
independence 0.014492754 .
litigation risk 0.004830918 0.005291005
nonaudit services 0.004830918 .
audit 0.005434783 .
audit effort . .
audit judgment . .
business risk . 0.017857143
experience . .
fraudulent financial reporting . .
assurance services . .
auditor tenure . 0.006802721
corporate social responsibility . .
going-concern . .
hypothesis generation . .
outsourcing . .
professional skepticism 0.005000000 .
audit report lag . .
auditor . .
auditor industry specialization . .
independence 0.005555556 .
litigation risk . .
nonaudit services . 0.012345679
audit 0.006250000 .
audit effort . .
audit judgment . .
business risk 0.006250000 0.006944444
experience . .
fraudulent financial reporting . .
assurance services . .
auditor tenure . .
corporate social responsibility . .
going-concern . .
hypothesis generation 0.007142857 .
outsourcing . .
professional skepticism . . .
audit report lag 0.006535948 . .
auditor . . .
auditor industry specialization . . .
independence . . .
litigation risk . . .
nonaudit services . . .
audit . . .
audit effort 0.007352941 0.015625 .
audit judgment . . .
business risk . 0.015625 .
experience . . .
fraudulent financial reporting . . .
assurance services . . .
auditor tenure . . .
corporate social responsibility . . .
going-concern . . .
hypothesis generation . . .
outsourcing . . .
professional skepticism . 0.006666667
audit report lag . .
auditor . .
auditor industry specialization . .
independence . .
litigation risk . .
nonaudit services . .
audit . .
audit effort 0.008333333 .
audit judgment . .
business risk . .
experience . .
fraudulent financial reporting . .
assurance services . .
auditor tenure . .
corporate social responsibility . .
going-concern . .
hypothesis generation 0.019047619 0.009523810
outsourcing . .
professional skepticism . .
audit report lag . .
auditor . .
auditor industry specialization . .
independence . 0.007936508
litigation risk 0.007407407 .
nonaudit services . .
audit . .
audit effort . .
audit judgment . .
business risk . .
experience . .
fraudulent financial reporting . .
assurance services . .
auditor tenure 0.009523810 .
corporate social responsibility . .
going-concern . .
hypothesis generation . .
outsourcing . .
professional skepticism . .
audit report lag . 0.008547009
auditor . .
auditor industry specialization . .
independence 0.008547009 .
litigation risk . .
nonaudit services . .
audit . .
audit effort . .
audit judgment . .
business risk . .
experience . .
fraudulent financial reporting . .
assurance services . .
auditor tenure . .
corporate social responsibility 0.010989011 .
going-concern . .
hypothesis generation . .
outsourcing 0.010989011 .
professional skepticism . . .
audit report lag . . .
auditor . . .
auditor industry specialization . . .
independence . . .
litigation risk . . .
nonaudit services . . .
audit . . .
audit effort . . .
audit judgment . . .
business risk . . 0.02083333
experience . 0.01041667 .
fraudulent financial reporting 0.009615385 . .
assurance services . . .
auditor tenure . . .
corporate social responsibility . . .
going-concern . . .
hypothesis generation 0.010989011 . .
outsourcing . . .
professional skepticism . . .
audit report lag . . .
auditor . . .
auditor industry specialization . . .
independence 0.009259259 . .
litigation risk . . .
nonaudit services . . .
audit . . .
audit effort . 0.01041667 .
audit judgment . . .
business risk . . .
experience . . .
fraudulent financial reporting . . .
assurance services . . 0.01298701
auditor tenure . . .
corporate social responsibility . . .
going-concern . . .
hypothesis generation . . .
outsourcing 0.023809524 . .
professional skepticism . ......
audit report lag . ......
auditor . ......
auditor industry specialization . ......
independence . ......
litigation risk . ......
nonaudit services . ......
audit . ......
audit effort . ......
audit judgment . ......
business risk . ......
experience . ......
fraudulent financial reporting . ......
assurance services . ......
auditor tenure . ......
corporate social responsibility . ......
going-concern . ......
hypothesis generation . ......
outsourcing . ......
$net$nodeDegree
$net$params
$subgraphs
[1] NA
$documentToClusters
$params
plot(Map$map)
It performs a Thematic Evolution Analysis based on co-word network analysis and clustering
years=c(2004,2020)
nexus <- thematicEvolution(All,field="DE",years=years,n=100,minFreq=2)
plotThematicEvolution(nexus$Nodes,nexus$Edges)
NA
CS <- conceptualStructure(All,field="DE", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)
# Create a historical citation network
options(width=130)
histResults <- histNetwork(All, min.citations = 1, sep = ";")
SCOPUS DB: Searching local citations (LCS) by document titles (TI) and DOIs...
Found 625 documents with no empty Local Citations (LCS)
# Plot a historical co-citation network
net <- histPlot(histResults, n=15, size = 10, labelsize=5)
Legend
Co-word analysis is a way to find the main ideas in a field of study by looking at the words that often appear together in scholarly articles.
We can use this method to create a map of the field, showing how the different concepts are related to each other.
In this example, we use a computer program to do the co-word analysis for us. The program also helps us to identify groups of documents that share common concepts.
CS <- conceptualStructure(All,field="DE_TM", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=5, documents=10)
Bradford’s law is a rule that says that the number of scientific articles on a given topic decreases rapidly as you look at more and more journals.
One way to think about it is to imagine that you have all the scientific journals on a given topic, and you sort them by the number of articles they have on that topic. The first journal will have the most articles, the second journal will have half as many articles, and so on.
Bradford’s law says that if you divide the journals into three groups, with each group having about one-third of the articles, then the number of journals in each group will be proportional to 1:2:4.
In other words, the first group will have the fewest journals, the second group will have twice as many journals, and the third group will have four times as many journals.
This is just a general rule, of course, and there will be some variation depending on the specific field of study. But Bradford’s law is a useful rule of thumb for estimating how many journals you need to search in order to find most of the relevant articles on a given topic.
bradford(All)
$table
$graph
The authors’ dominance ranking from a as proposed by Kumar & Kumar, 2008.
dominance(results, k = 10)
Where
RPYS for detecting the Historical Roots of Research Fields. The method was introduced by Marx et al., 2014.
rpys(All, sep = ";", timespan = NULL, graph = T)
$spectroscopy
$rpysTable
$CR
$df
NA
Marx, W., Bornmann, L., Barth, A., & Leydesdorff, L. (2014). Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS). Journal of the Association for Information Science and Technology, 65(4), 751-764.
Massimo Aria and Corrado Cuccurullo, A brief introduction to bibliometrix, https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html
bibliometrix: Comprehensive Science Mapping Analysis, https://cran.r-project.org/web/packages/bibliometrix/index.html
Package ‘bibliometrix’, https://cran.r-project.org/web/packages/bibliometrix/bibliometrix.pdf