Following the framework explained in the previous section, the authors created a comparative narrative report based on four areas of knowledge, respectively. The network mapping process generated the comparative outcome illustrated in Figure 1.
E-commerce
The network generated is illustrated in Figure 1 and is based on the most important footprints related to the e-commerce field. The nodes represent the cited references, and the links are co-citations.
Running the project, 1,570 references were analysed, in the final, 1,559 were qualified records (2000-2022). To visualize some relevant groupings for this knowledge field, the clustering process generated 280 groups with a top of research lines labelled according to the dimension of each one. The authors characterized the nature of the groupings by choosing to select noun phrases from the titles and showing labels by log-likelihood ratio (LLR). The cluster labels are shown as follows in Figure 2.
The modularity Q (0.9559) and the mean Silhouette S (0.9806), the metrics that describe the structural properties of the network shows a high homogeneity of the clusters and dense connections between nodes within them. So, the network is divided into five co-citations clusters. The most relevant ones with their sizes and top terms are presented as follows in Table 2.
Table 2 Top terms by cluster labels and indicators for e-commerce.
Cluster ID
|
Size
|
Silhouette
|
Mean (Year)
|
Label (LLR)
|
1
|
31
|
0.962
|
2016
|
e-commerce adoption (18.1, 1.0E-4); influencing beliefs formation
(15.79, 1.0E-4); social commerce (15.79, 1.0E-4); sme travel agencies (15.79, 1.0E-4); mobile commerce adoption (13.49, 0.001)
|
2
|
28
|
1
|
2012
|
social media (23.99, 1.0E-4); latent transition analysis (23.99, 1.0E4); trip experience (23.99, 1.0E-4); tourism design (19.05, 1.0E-4); smart tourism development (14.18, 0.001)
|
3
|
27
|
0.985
|
2018
|
opinion mining (21.68, 1.0E-4); fuzzy logic (21.68, 1.0E-4); salient research topics (17.97, 1.0E-4); analyzing e-wom (14.31, 0.001); stochastic dominance (14.31, 0.001)
|
6
|
22
|
0.978
|
2017
|
attention-based item collaborative (21.02, 1.0E-4); fast shipping ecommerce (18.33, 1.0E-4); case study (18.33, 1.0E-4); inbound
|
|
|
|
|
logistics operation (18.33, 1.0E-4); purchasing attitude (15.65, 1.0E-4)
|
10
|
18
|
0.978
|
2015
|
empirical investigation (22.2, 1.0E-4); big data perspective (22.2, 1.0E-4); online review helpfulness (22.2, 1.0E-4); specific word entropy (17.64, 1.0E-4); purchasing behaviour (13.14, 0.001)
|
To illustrate where are the major areas of research based on the dataset, some adjustments were made to generate the following structure: the largest cluster (#1) has thirty-one members and a silhouette value of 0.962. It is labelled as e-commerce adoption by LLR. The most relevant citer to the cluster is Shemi, Alice P. (2018) [54] with the paper entitled ‘e-commerce and entrepreneurship in SMEs: case of mybot’ [52]. The top ranked item by citation counts is Rogers E.M. (2010) in Cluster #4, with eleven citations. Others relevant references are presented in Table 3.
Table 3 Top references in e-commerce by citations count.
Citation count
|
References
|
Cluster ID
|
11
|
Rogers EM, 2010, DIFFUSION INNOVATION, 0, 0 [53]
|
4
|
8
|
Molla A, 2005, INFORM MANAGE-AMSTER, 42, 877
|
16
|
8
|
Wang D, 2012, J TRAVEL RES, 51, 371
|
2
|
7
|
Xiang Z, 2010, TOURISM MANAGE, 31, 179
|
2
|
To find what are the most active areas in the e-commerce field of knowledge, a burst detection was performed. To do so, the reference node was selected to showcase the landmarks papers in this field by this indicator. So, the top-ranked item is Molla A. (2005) [54] in Cluster #16, with bursts of 4.76, followed by other authors presented in Table 4.
Table 4 Burst detection by reference node for e-commerce.
Bursts
|
References
|
DOI
|
Cluster ID
|
4.76
|
Molla A, 2005, INFORM MANAGE-AMSTER, 42, 877[54]
|
10.1016/j.im.2004.09.002
|
16
|
4.24
|
Rogers EM, 2010, DIFFUSION INNOVATION, 0, 0
|
|
4
|
3.93
|
Wang D, 2012, J TRAVEL RES, 51, 371
|
10.1177/0047287511426341
|
2
|
3.75
|
Burke R, 2002, USER MODEL USER-ADAP, 12, 331
|
10.1023/A:1021240730564
|
85
|
3.43
|
Sigala M, 2012, NEW DIRECT TOUR ANAL, 0, 1
|
|
2
|
3.27
|
Wober KW, 2006, DESTINATION RECOMMENDATION
SYSTEMS: BEHAVIOURAL FOUNDATIONS AND APPLICATIONS, 0, 205
|
10.1079/9780851990231.0205
|
5
|
3.23
|
Devlin J, 2019, P 2019 C N AM CHAPT, 0, 4171
|
|
6
|
3.19
|
Gefen D, 2003, MIS QUART, 27, 51
|
10.2307/30036519
|
4
|
Another useful result is related to the centrality indicator that shows how the major areas are connected. The top-ranked items by it are presented in Table 5.
Table 5 Centrality of major areas for e-commerce.
Centrality
|
References
|
DOI
|
Cluster ID
|
36
|
Bordonaba-Juste V, 2009, SUPPLY CHAIN MANAG, 14, 393 [55]
|
10.1108/13598540910980305
|
0
|
36
|
Bastl M, 2012, INT J OPER PROD MAN, 32, 650
|
10.1108/01443571211230916
|
0
|
36
|
Bennett D, 2012, INT J OPER PROD MAN, 32, 1281
|
10.1108/01443571211274558
|
0
|
36
|
Cai SH, 2010, J OPER MANAG, 28, 257
|
10.1016/j.jom.2009.11.005
|
0
|
22
|
Rahayu R, 2015, WORLD CONFERENCE ON TECHNOLOGY, 0, 142
|
10.1016/j.sbspro.2015.06.423
|
1
|
22
|
Capo-Vicedo J, 2011, SUPPLY CHAIN MANAG, 16, 284
|
10.1108/13598541111139099
|
0
|
Also, the burst detection by keywords determined the authors to present the big picture of the topics of interest in the e-commerce field (Table 6).
Table 6 Burst detection by keywords for e-commerce.
Keywords
|
Strengths
|
Begin
|
End
|
2000 - 2022
|
social media
|
5.89
|
2016
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂
|
|
online review
|
5.82
|
2020
|
2022
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃
|
|
destination marketing
|
5.59
|
2007
|
2011
|
▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂
|
|
web service
|
4.93
|
2001
|
2005
|
▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
|
|
e commerce
|
4.65
|
2011
|
2013
|
▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂
|
|
social network
|
4.64
|
2015
|
2017
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂
|
|
sentiment analysis
|
4.58
|
2018
|
2022
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃
|
|
information search
|
4.34
|
2009
|
2014
|
▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂
|
|
service
|
3.98
|
2019
|
2022
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃
|
|
web
|
3.97
|
2011
|
2017
|
▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂
|
|
perceived risk
|
3.93
|
2018
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂
|
|
data mining
|
3.72
|
2012
|
2015
|
▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂
|
|
purchase intention
|
3.61
|
2019
|
2022
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃
|
|
online shopping
|
3.49
|
2014
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂
|
|
experience
|
3.34
|
2017
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂
|
|
electronic commerce
|
3.28
|
2008
|
2013
|
▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂
|
|
tourism
|
3.18
|
2008
|
2017
|
▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂
|
|
Generating the top keywords report, the authors wanted to discover more details about it, so mapping of clusters (Figure 3) by keyword node was performed. The results exhibit 146 clusters for the same time span and the selection criteria represented by Top 10% per slice and 1.00% nodes labelled, without pruning (network, N= 18325, E=57424, and density = 0.0003).
Table 7 Top authors by burst detection.
Authors
|
Strength
|
Begin
|
End
|
2000 - 2022
|
DANIEL R FESENMAIER
|
5.78
|
2010
|
2017
|
▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▂▂▂▂▂
|
|
ZHENG XIANG
|
4.18
|
2010
|
2017
|
▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▂▂▂▂▂
|
|
OK KWON
|
3.4
|
2003
|
2005
|
▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
|
|
HJ HWANG
|
3.4
|
2003
|
2005
|
▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
|
|
ALLAM MAALLA
|
3.18
|
2018
|
2020
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂
|
|
OUYANG YI
|
3.62
|
2006
|
2007
|
▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
|
|
GHEORGHE MILITARU
|
3.5
|
2018
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂
|
|
VALENTINANDREI MANESCU
|
3.5
|
2018
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂
|
|
By title node, e-commerce is divided into thirteen co-citations clusters for 1,559 qualified records. The timeline view illustrates the evolution of the main topics of research as follows in Figure 4.
According to Table 8, where the largest five clusters are summarized, the first one (#0) has forty-two members and according to LLR, it is labelled as repurchase behaviour. The most relevant citer in this group is Putra, Panca O. Hadi [56] with their entitled paper ‘Contextual factors and performance impact of e-business use in Indonesian SMEs’.
Table 8. Most relevant citers in e-commerce by clusters.
Cluster ID
|
Size
|
Silhouett
e
|
Label (LLR)
|
Year
|
The most relevant citer
|
0
|
42
|
0.643
|
repurchase behavior
(144.12, 1.0E-4)
|
2011
|
Putra, Panca O Hadi (2020.0) Contextual factors and
performance impact of e-business use in indonesian small and
medium enterprises (smes). HELIYON DOI 10.1016/j.heliyon.2020.e03568. [56]
|
1
|
42
|
0.727
|
developing country
(327.66, 1.0E-4)
|
2010
|
Li, Bo (2020.0) Key influencing factors of consumers' vegetable e-commerce adoption willingness, behavior, and willingness-behavior consistency in Beijing, China. BRITISH FOOD JOURNAL, V122, P16 DOI 10.1108/BFJ-11-2019-0834. [57]
|
2
|
38
|
0.736
|
search engine marketing
(255.83, 1.0E-4)
|
2012
|
Fesenmaier, Daniel R (2011.0) A framework of search engine use for travel planning. JOURNAL OF TRAVEL RESEARCH, V50, P15 DOI 10.1177/0047287510385466. [58]
|
3
|
31
|
0.586
|
purchase intention
(194.09, 1.0E-4)
|
2015
|
Chatterjee, Swagato (2021.0) Exploring healthcare/health-
product ecommerce satisfaction: a text mining and machine learning application. JOURNAL OF BUSINESS RESEARCH, V131, P11 DOI 10.1016/j.jbusres.2020.10.043. [59]
|
Cybersecurity
The labelling process of the clusters for cybersecurity was based on 10,245 qualified records and generated 234 groups with high homogeneity (S = 0.9268). Table 9 shows that the size of each cluster is relevant enough to present the major research lines in this field that are pictured in Figure 5.
Table 9 Clusters of cybersecurity labelled by major research lines.
Cluster ID
|
Size
|
Silhouette
|
Mean (Year)
|
Label (LLR)
|
0
|
99
|
0.92
|
2016
|
human factor (1038.26, 1.0E-4); machine learning (760.94, 1.0E-4); health care (678.93, 1.0E-4); scoping review (601.43, 1.0E-4); smart grid (593.5, 1.0E-4)
|
1
|
95
|
0.915
|
2017
|
network intrusion detection (1797.74, 1.0E-4); using machine (933.96, 1.0E4); objective comparison (863.01, 1.0E-4); iot network (829.85, 1.0E-4); intrusion detection system (790.24, 1.0E-4)
|
2
|
78
|
0.873
|
2016
|
blockchain technology (2099.46, 1.0E-4); smart cities (1690.93, 1.0E-4); blockchain technologies (928.62, 1.0E-4); iot device (826.53, 1.0E-4); using blockchain (643.47, 1.0E-4)
|
3
|
69
|
0.914
|
2015
|
adversarial machine learning (1756.66, 1.0E-4); adversarial example
(1053.93, 1.0E-4); deep learning (861.22, 1.0E-4); machine learning (854.89, 1.0E-4); adversarial attack (660.04, 1.0E-4)
|
4
|
65
|
0.884
|
2016
|
industrial control system (2556.12, 1.0E-4); in-vehicle network (1172, 1.0E4); attack detection (894.82, 1.0E-4); case study (782.27, 1.0E-4); behavioral model (770.93, 1.0E-4)
|
|
|
|
|
national cybersecurity (682.86, 1.0E-4); shared responsibility (610.77, 1.0E-
|
5
|
61
|
0.944
|
2014
|
4); global cybersecurity (538.74, 1.0E-4); political economy (472.75, 1.0E-
|
|
|
|
|
4); theorizing cyber coercion (466.76, 1.0E-4)
|
6
|
54
|
0.949
|
2012
|
load redistribution attack (1494.73, 1.0E-4); advanced metering
infrastructure (747.5, 1.0E-4); power system adequacy assessment (741.01,
|
|
|
|
|
1.0E-4); power grid (656.86, 1.0E-4); 3d printing cybersecurity (656.86, 1.0E-4)
|
Table 10 Major themes for cybersecurity by clusters and references.
Research line
|
Citer
|
Research paper
|
Human factor
|
Koroniotis, Nickolaos (2020.0)
|
A holistic review of cybersecurity and reliability perspectives in smart airports. IEEE ACCESS DOI 10.1109/ACCESS.2020.3036728. [60]
|
Network intrusion detection
|
Gamage, Sunanda (2020.0)
|
Deep learning methods in network intrusion detection: a survey and an objective comparison. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS[61]
|
Blockchain technology
|
Ali, Muhammad Salek (2019.0)
|
Applications of blockchains in the internet of things: a comprehensive survey. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, V21, P42 DOI 10.1109/COMST.2018.2886932. [62]
|
Adverisal machine learning
|
Dasgupta, Dipankar (2020.0)
|
Machine learning in cybersecurity: a comprehensive survey. JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS
METHODOLOGY TECHNOLOGY-JDMS DOI 10.1177/1548512920951275. [63]
|
National cybersecurity
|
Lindsay, Jon Randall (2017.0)
|
Restrained by design: the political economy of cybersecurity. DIGITAL POLICY REGULATION AND GOVERNANCE, V19, P22 DOI 10.1108/DPRG-05-2017-0023. [64]
|
To see how the major areas are interconnected (Figure 6), the betweenness centrality analysis was used. By the reference node, the item with the higher centrality (47) is associated with Diro A.A. (2018), [65] which is ranked in Cluster #1. The other centres of reference nodes are presented in Table 11.
Table 11. Central references by clusters for cybersecurity.
Centrality
|
References
|
DOI
|
Cluster ID
|
47
|
Diro AA, 2018, FUTURE GENER COMP SY, 82, 761
|
10.1016/j.future.2017.08.043
|
1
|
43
|
Yin CL, 2017, IEEE ACCESS, 5, 21954
|
10.1109/ACCESS.2017.2762418
|
1
|
41
|
Chaabouni N, 2019, IEEE COMMUN SURV TUT, 21, 2671
|
10.1109/COMST.2019.2896380
|
1
|
38
|
LeCun Y, 2015, NATURE, 521, 436
|
10.1038/nature14539
|
1
|
37
|
Shone N, 2018, IEEE TETCI, 2, 41
|
10.1109/TETCI.2017.2772792
|
1
|
Afterward, the authors discovered what are the most active areas in cybersecurity with the help of burst detection for reference nodes. The results are presented in Table 12.
Table 12 References for most active areas in cybersecurity.
Burst
|
References
|
DOI
|
Cluster ID
|
13.24
|
Wang WY, 2013, COMPUT NETW, 57, 1344
|
10.1016/j.comnet.2012.12.017
|
6
|
11.45
|
Pasqualetti F, 2013, IEEE T AUTOMAT CONTR, 58, 2715
|
10.1109/TAC.2013.2266831
|
4
|
10.87
|
Schmitt MN, 2013, TALLINN MANUAL INT L, 0, 0
|
-
|
10
|
In order to enable a comparison between e-commerce and cybersecurity fields of research by keyword node, a new analysis for 10,242 qualified records was run. The major themes for cybersecurity are divided into sixty-nine clusters with the most three preeminent presented in Table 13.
Table 13 Labels and clusters for cybersecurity by keyword node.
Cluster ID
|
Size
|
Silhouette
|
Mean (Year)
|
Label (LLR)
|
0
|
142
|
0.698
|
2017
|
cybersecurity awareness (4192.79, 1.0E-4)
|
1
|
132
|
0.628
|
2017
|
smart cities (5279.12, 1.0E-4)
|
2
|
105
|
0.725
|
2016
|
machine learning (6747.1, 1.0E-4)
|
The Silhouette means shows a relatively high homogeneity, which is associated with large sizes of the clusters. So, the keyword nodes associated with the most relevant citers for each cluster highlight the impact of their work (Table 14) on the related topic of research.
Table 14 Most relevant citers and the major research lines for cybersecurity.
Research line
|
Citer
|
Research paper
|
cybersecurity awareness
(4192.79, 1.0E-4)
|
Liagkou, Vasiliki (2021.0)
|
Challenges and Opportunities in Industry 4.0 for Mechatronics, Artificial Intelligence and Cybernetics. Electronics 2021, 10, 2001. https://doi.org/10.3390/electronics10162001 [66]
|
smart cities
(5279.12, 1.0E-4)
|
Hussain, S M Suhail (2020.0)
|
A review of iec 62351 security mechanisms for iec 61850
message exchanges. IEEE TRANSACTIONS ON
INDUSTRIAL INFORMATICS, V16, P12 DOI 10.1109/TII.2019.2956734. [67]
|
machine learning
(6747.1, 1.0E-4)
|
Mishra, Vishrut Kumar
(2019.0)
|
A modeling framework for critical infrastructure and its application in detecting cyber-attacks on a water distribution system. INTERNATIONAL JOURNAL OF CRITICAL
INFRASTRUCTURE PROTECTION DOI 10.1016/j.ijcip.2019.05.001. [68]
|
By comparing e-commerce with cybersecurity (Figure 7), the authors aimed to find a possible nexus path between them. Analysing the models, cluster #0, cybersecurity awareness in cybersecurity, and cluster #2, e-commerce adoption in e-commerce emerged.
Both seem to represent major areas in each network, so, a detailed analysis by keyword nodes was performed for
further analysis of cybersecurity knowledge. The top keywords are shown in Table 15.
Table 15 Top keywords for cybersecurity research line.
Keywords
|
Strengths
|
Begin
|
End
|
2000 - 2022
|
cyber security
|
19.22
|
2012
|
2017
|
▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂
|
|
smart grid
|
12.36
|
2010
|
2017
|
▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▂▂▂▂▂
|
|
cybersecurity education
|
7.76
|
2014
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂
|
|
vulnerability assessment
|
6.28
|
2005
|
2018
|
▂▂▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂
|
|
information security
|
6.21
|
2008
|
2014
|
▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂
|
|
moving target defense
|
5.88
|
2016
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂
|
|
cloud computing
|
5.82
|
2011
|
2017
|
▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂
|
|
cyber defense
|
5.42
|
2014
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂
|
|
information sharing
|
5.42
|
2015
|
2017
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂
|
|
static analysis
|
5.17
|
2018
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂
|
|
computer security
|
5.09
|
2009
|
2015
|
▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂
|
|
critical infrastructure
|
5
|
2015
|
2017
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂
|
|
security
|
4.99
|
2013
|
2015
|
▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂
|
|
crime
|
4.94
|
2017
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂
|
|
attack graph
|
4.93
|
2012
|
2018
|
▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂
|
|
data protection
|
4.89
|
2017
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂
|
|
social network
|
4.87
|
2016
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂
|
|
big data
|
4.83
|
2012
|
2018
|
▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂
|
|
software security
|
4.81
|
2016
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂
|
|
game theory
|
4.63
|
2014
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂
|
|
situation awareness
|
4.46
|
2016
|
2019
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂
|
|
smart home
|
4.42
|
2019
|
2020
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂
|
|
critical infrastructure protection
|
4.4
|
2015
|
2016
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂
|
|
malware
|
4.16
|
2019
|
2020
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂
|
|
cybersecurity training
|
4.13
|
2017
|
2018
|
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂
|
|
The top-ranked item by citation counts is Yang Liu (2015) in Cluster #0, with eighty citations (Figure 8). He presents the best centrality, in the same cluster with the value of 22. He is followed by Cheewooi Ten (2007) with a centrality of 11 in cluster #1. In terms of burst, the top-ranked author is Hsinchun Chen (2014) in Cluster #5, with bursts of 10.69.
Digital resilience and economic intelligence management
With the network creation process, 339 clusters were created and labelled for digital resilience research knowledge (Figure 9). The mapping aimed to identify what are the major discussion topics by reference node by analyzing 1,559 qualified records. The results present a Mean silhouette S = 0.9565 and Modularity Q = 0.8963. Figure 9 shows the results with the overall clarity of the decomposed network (Modularity Q = 0.8963) and the quality of the cluster configuration (Mean silhouette S = 0.9565), unveiling the following topics of research: COVID-19 pandemic, digital technologies, future trends, and other themes detailed in Table 16.
Table 16 Labels for major areas in digital resilience.
Cluster ID
|
Size
|
Silhouette
|
Mean (Year)
|
Label
|
0
|
90
|
0.935
|
2018
|
covid-19 pandemic; covid-19 supply chain management; lean resilience; active usage; systematic literature review | sustainable supply chain; supply chain resilience; ripple effect; to-end visibility; digital supply chain management
|
3
|
36
|
0.987
|
2018
|
digital technologies; future trend; microgrid digital twin; digital twin; supply chain
| data science platform; exploratory study; healthcare service; universal manufacturing; cognitive digital twin
|
5
|
24
|
0.972
|
2016
|
supply chain; new perspective; resilience; managing disruption risk; new disruption risk management | managing disruption risk; supply chain; new disruption risk management; new perspective; resileanness
|
7
|
18
|
0.97
|
2017
|
ripple effect; digital supply chain; empirical case studies; managing disruption; supply chain risk analytics | learning approach; digital manufacturing; resilient supplier selection; supervised machine; digital supply chain
|
The top-ranked reference by bursts is Ivanov D. (2019) in Cluster #0, with bursts of 15.80 (Table 17).
Table 17 The references by bursts for digital resilience.
Burst
|
References
|
DOI
|
Cluster ID
|
15.80
|
Ivanov D, 2019, INT J PROD RES, 57, 829
|
10.1080/00207543.2018.1488086
|
0
|
14.49
|
Ivanov D, 2020, INT J PROD RES, 58, 2904
|
10.1080/00207543.2020.1750727
|
0
|
10.75
|
Ivanov D, 2020, ANN OPER RES, 0, 0
|
10.1007/s10479-020-03640-6
|
0
|
10.02
|
Dolgui A, 2018, INT J PROD RES, 56, 414
|
10.1080/00207543.2017.1387680
|
0
|
In addition, the analysis of the economic intelligence management research topic generated an outcome very similar to digital resilience and it presents 319 homogeneous clusters. The labels of the most cited groups are shown in Table 18 and visually interconnected in Figure 10.
Table 18 The labels for the most cited references by clusters.
Cluster ID
|
Size
|
Silhouette
|
Mean (Year)
|
Label
|
0
|
70
|
0.911
|
2018
|
covid-19 pandemic (148.8, 1.0E-4); artificial intelligence (89.47, 1.0E-4); comprehensive review (48.8, 1.0E-4); literature review (46.09, 1.0E-4); open challenge (40.62, 1.0E-4)
|
1
|
36
|
0.983
|
2017
|
artificial intelligence (169.05, 1.0E-4); composite link (70.47, 1.0E4); technological change (70.47, 1.0E-4); artificial intelligence firm (67.13, 1.0E-4); consumer behavior (67.13, 1.0E-4)
|
2
|
35
|
0.981
|
2014
|
big data (119.86, 1.0E-4); debating big data (75.7, 1.0E-4); realizing value (75.7, 1.0E-4); intellectual capital (70.24, 1.0E-4); supply chain management (59.35, 1.0E-4)
|
3
|
27
|
0.989
|
2017
|
sustainability perspective (76.56, 1.0E-4); systematic literature
review (76.56, 1.0E-4); technologies assessment (76.56, 1.0E-4); trends challenge (66.27, 1.0E-4); new perspective (66.27, 1.0E-4)
|
4
|
24
|
0.966
|
2016
|
travel tourism (68.22, 1.0E-4); biosecurity crisis management
automation (68.22, 1.0E-4); hospitality companies (68.22, 1.0E-4);
conceptual framework (68.22, 1.0E-4); economic performance (68.22, 1.0E-4)
|
|
|
|
|
IoT-based personalized healthcare service (42.01, 1.0E-4); multicenter data fusion (31.34, 1.0E-4); medical explainable (31.34, 1.0E4); rural financial development (20.79, 1.0E-4); rural governance
|
8
|
16
|
0.998
|
2018
|
(20.79, 1.0E-4)
|
|
|
|
|
energy building (85.43, 1.0E-4); to-peer energy sharing framework
|
|
|
|
|
(46.83, 1.0E-4); to-peer energy sharing management (37.34, 1.0E-4);
sustainable local energy trading (27.92, 1.0E-4); demurrage
|
12
|
13
|
0.958
|
2017
|
mechanism (27.92, 1.0E-4)
|