Introduction

Materials and Methods

Results and Discussions

Author Keywords Co-Occurrence Analysis for All Journals in the Sample

Figure 2 shows the results of clustering based on co-occurrence of Author keywords obtained by VOSviewer using the following parameters: the occurrence threshold for terms is 3, the number of terms for plotting is 500, the minimum number of terms in a cluster is 60. In this case, 5 clusters were obtained. In total there were 10300 Author keywords, 984 meet and more 3 times.

Figure 2. Author keywords co-occurrence network for all journals in the sample.

Figure 2. Author keywords co-occurrence network for all journals in the sample.

Table 1 shows 15 Author keywords typical for new publications. It is obtained by sorting the field score<Avg. pub. year>. For ease of reading, the tables have abbreviated headings, e.g., score<Avg. pub. year> → Avg.pub.year.

The term ‘incentive mechanism’ is typical of the newest publications.

Citation from paper [11] revealing the context for the term ‘incentive mechanism’: “Under the decentralized decision–making mode, the incentive mechanism positively promotes the innovation efforts of both parties of cooperative enterprises, while the local government is not affected, while under the centralized decision–making mode, the incentive mechanism positively affects the innovation efforts and innovation benefits of all three parties”.

Table 2 shows 16 Author keywords typical for publications with the highest average citation. Obtained by sorting the field score<Avg. norm. citations>.

The terms ‘digital concrete’ and ‘3d concrete printing’ often appear in highly cited journals, so let’s give the context of their use [12]. “Digital concrete technologies, such as 3D concrete printing, have the potential to be game changers in the construction industry by reducing material consumption and offering a high level of automation.”

Diagrams Creation with Scimago Graphica Program

Figures 2x1–5 were plotted using the program Scimago Graphica for each of the 5 clusters shown in Figure 2. Technically, this was done by filtering the data by cluster parameter. In order to reduce the number of terms in the graph and to obtain a connected graph, an additional filter was applied to filter out terms that had a ‘total link strength’ parameter less than or equal to 30. The ‘cluster 1–4’ in the legend on the graphs reflect the grouping of terms implemented by the algorithm built into Scimago Graphica. They should not be confused with the clusters of Figure 2, as they are an additional grouping of terms. The use of the ‘Convex hull’ allows to more clearly highlight the terms that are on its boundary, i.e. that take extreme values for a given group of terms.

The term networks in Figures 2.1–5 were constructed without using any layout method, in the coordinates ‘Avg.pub.year’ vs ‘Avg.norm.citations’, which facilitated the selection of terms that could be interpreted as reflecting topical research issues. We could not find examples of this kind of term network construction in the works of other authors.

Figure 2.1. Network of Author keywords from the first cluster of Figure 2.

Figure 2.1. Network of Author keywords from the first cluster of Figure 2.

The terms ‘renewable energy, digital economy and energy efficiency’ may describe relevant research topics, such as those presented in the following papers [13, 14].

Figure 2.2. Network of Author keywords from the second cluster of Figure 2.

Figure 2.2. Network of Author keywords from the second cluster of Figure 2.

In this case, the topic ‘digital twin’ with the most relevant terms ‘electric vehicle and cyberphysical system’ is best represented. An example of an article revealing this topic is [15], cited 271 times according to ScienceDirect and 397 times according to GOOGLE. Current as of 10–10–2024.

Figure 2.3. Network of Author keywords from the third cluster of Figure 2.

Figure 2.3. Network of Author keywords from the third cluster of Figure 2.

This group of terms is dominated by the topic ‘additive manufacture’. And ‘wire arc manufacture’ is characteristic of new publications, and ‘digital fabrication’ which are more frequently cited. The articles [16, 17] are relevant examples that give the context of the use of these terms.

Figure 2.4. Network of Author keywords from the fourth cluster of Figure 2.

Figure 2.4. Network of Author keywords from the fourth cluster of Figure 2.

Cluster 2 contains not only the maximum number of terms but also the most topical terms ‘build information modeling’ and ‘industry foundation class’ (IFC is a standardized, digital description of the built asset industry).

Context for these terms: “This research explores the application of Building Information Modeling (BIM) in construction and demolition waste (CDW) management and the sustainability of buildings. The method consisted of applying Design Science Research (DSR) to develop a conceptual information model based on the Industry Foundation Classes (IFC)” [18]. “Building Information Modeling (BIM) plays a pivotal role in the construction management of dam engineering projects … while Industry Foundation Classes (IFC) offer a standardized digital representation of the built environment” [19].

This graph clearly shows the importance of keyword normalization: the terms ‘build information modeling’ and ‘build information model’ have a similar meaning, but are placed on different sides of the second cluster. In more general graphs, such as Figure 2, this problem may not be noticeable, but a closer look, Figure 2.4, shows that the lack of normalization significantly affects the clustering results.

Figure 2.5. Network of Author keywords from the fifth cluster of Figure 2.

Figure 2.5. Network of Author keywords from the fifth cluster of Figure 2.

‘Smart contract’, ‘blockchain’ and ‘Industry 5.0’ describes the topical theme of this cluster.

Context for the given terms: “the Blockchain concepts and their applications in Marketing through bibliometrics, network, and thematic analyses, which can provide several novel insights … by evaluating the most significant and cited research publications, keywords, institutions, authors’ collaboration network, and finally countries that promote Industry 5.0 (I5.0) businesses” [20]. For bibliometric research, the topic of using ‘Blockchain concepts’ for ‘Marketing through bibliometrics’ deserves a separate, more in–depth consideration. [21] — “propose intelligent smart city architecture for vaccine manufacturing with a Smart Contract–based access control model”.

Author Keywords Co-Occurrence Analysis for Journals in Q1

Next, let’s look separately at the distribution of Author keywords for the journals included in Q1, Figure 3.

Figure 3. Author keyword co-occurrence network for Q1 journals.

Figure 3. Author keyword co-occurrence network for Q1 journals.

Number of entries for journals included in Q1 — 2698.

Clustering parameters:8263 terms meet 5; 702 meet 3; 500 terms in use; 4 clusters for 50 terms min in cluster.

Table 3 shows 15 Author keywords that are typical for new publications for the journals included in Q1. Obtained by sorting the field score<Avg. pub. year>.

NFTs (or “non–fungible tokens”) are a special kind of cryptoasset in which each token is unique

The terms ‘Incentive mechanism’, NFTs, ‘technology acceptance model’ reflect the current topic according to this table.

The context for these terms is disclosed in the articles: [22] — “The model captures the core categories that affect trust toward crypto–tokens applications ... to develop strategies that ... create value with crypto–tokens and Web3.0 economies”. “Actually, the Technology acceptance model (TAM) was initially formulated by Davis to explore the relationships among the elements like “perceived usefulness” (PU), “perceived ease of use” (PEOU), Attitude (ATT), Behavioural Intention (BI) and actual use (AU)” [23].

Table 4. Author keywords specific to publications of Q1 journals with high average normalized citation rate.

Table 4. Author keywords specific to publications of Q1 journals with high average normalized citation rate.

label	cluster	Occurrences	Avg.pub.year	Avg.norm.citations
data	1	4	2023.25	2.245
battery	2	5	2023.6	2.1968
information fusion	2	4	2023.25	2.0612
sensor	2	5	2022.8	1.8612
energy transition	1	9	2023.222	1.6502
digital concrete	3	8	2022.875	1.567
financial performance	1	4	2022.75	1.5522
remain useful life	2	3	2023.333	1.5301
electronic	2	4	2022.75	1.5294
evaluation	1	3	2022.333	1.5209
federate learn	2	7	2023.714	1.4955
process plan	2	3	2023	1.4939
ambidexterity	1	3	2022.333	1.4895
patent	1	3	2022.667	1.4493
text analysis	4	5	2023.8	1.4442

The terms ‘data’, ‘battery’, ‘information fusion’ define the relevance topic from this table. The title of the paper itself [24] reveals the context of these terms – ‘Multi sensor fusion methods for state of charge estimation of smart lithium–ion batteries’.

Diagrams Created with the Scimago Graphica

Figures 3x1–4 are plotted using the Scimago Graphica program for each of the 4 clusters shown in Figure 3 in a similar way to Figures 2x1–5.

Figure 3.1. Network of Author keywords from the first cluster of Figure 3.

Figure 3.1. Network of Author keywords from the first cluster of Figure 3.

Digital economy, especially ‘digital economy and supply chain’, are among the largest clusters of emerging topics in Q1 journal publications.

An example of a publication reflecting this theme is the article [25] showed that a study of Chinese public companies from 2007 to 2020 found a positive correlation between the level of digital transformation of large customers and the level of digital transformation of their suppliers, especially if the customers are important to the suppliers.

The authors of [26] have shown that the “The result shows that “absence of urgency for supply chain digitalization”, lack of proper innovation strategies”, and “Inadequate leadership to lead digital transformation “are the highest–ranked digital supply chain barriers that needs to be overcome on a priority basis”.

Figure 3.2. Network of Author keywords from the second cluster of Figure 3.

Figure 3.2. Network of Author keywords from the second cluster of Figure 3.

‘Cyberphysical systems’ and ‘Industry 5.0’ are the topics of interest in this cluster.

Context for these terms: “… transforming industrial automation into industry 5.0 compliances to attain full autonomy with minimal human intervention … Smart Cyber–Physical System (SCPS) is the most important part of the fourth industrial revolution, where diffident programmed embedded systems are networked together to perform, computation, communication, control, and actuation” [27]. Further details on this topic can be found in Special issue. Cyber–Physical System for Autonomous Process Control in Industry 5.04.

Figure 3.3. Network of Author keywords from the third cluster of Figure 3.

Figure 3.3. Network of Author keywords from the third cluster of Figure 3.

The theme: ‘digital fabrication’, ‘digital light process’ is the most cited topic in this cluster and is close to the similar theme presented in Figure 2.3. At the same time, ‘wire arc manufacture’ was separated into a different topic. This example shows how clustering depends on the chosen data slice, emphasizing that filters are part of the question we want to answer.

Figure 3.4. Network of Author keywords from the fourth cluster of Figure 3.

Figure 3.4. Network of Author keywords from the fourth cluster of Figure 3.

It can be seen here that the topic ‘augment realty’ appears frequently in new publications, but does not have a high citation rate. The topic ‘build information models’ has already appeared in Figure 2.4 under the spellings ‘build information modeling’ and ‘build information model’, which again shows the importance of the keyword preparation stage in cluster analysis.

Author Keywords Co-Occurrence Analysis for Journals Not in Q1

The co-occurrence clustering of Author keywords shown in Figure 4 is constructed on the basis of 968 records with the following parameters: 2919 of all terms, 100 meet 5 and more times, 230 meet 3 and more times, give 4 clusters for 30 min terms in cluster.

Figure 4. Author keyword co-occurrence network for non–Q1 journals.

Figure 4. Author keyword co-occurrence network for non–Q1 journals.

The total number of publications not included in Q1 was less than those included: 2698 and 968, respectively. Therefore, the ‘total link strength’ filter was not used for the Author keywords of such publications when constructing the 4x1–4 graphs. Taking into account that the purpose of this paper was not to analyze the topic of digital industry in detail, but to demonstrate the possibilities of the proposed research approach, not using this filter allowed us to show the reader how graphs reflecting a larger number of terms can look like. Only the ‘cluster’ filter was used.

Table 5. Author keywords specific to new publications for journals not included in the Q1.

Table 5. Author keywords specific to new publications for journals not included in the Q1.

label	cluster	Occurrences	Avg.pub.year	Avg.norm.citations
strategic management	1	3	2024	1.2048
energy efficiency	1	5	2023.8	1.07
digital product passport	2	4	2023.75	1.1367
aas	2	3	2023.667	0.7873
digital technology	1	9	2023.667	1.0157
education	1	3	2023.667	0.9133
healthcare	1	3	2023.667	1.244
hybrid model	2	3	2023.667	0.9881
life cycle assessment	2	3	2023.667	0.9291
logistic	3	3	2023.667	0.9842
product development	1	3	2023.667	0.9842
transfer learn	4	3	2023.667	1.0471
modeling	2	5	2023.6	0.94
innovation	1	7	2023.571	0.9785
cloud manufacture	3	4	2023.5	0.9968

Table 6. Author keywords specific to publications of not in Q1 journals with high average normalized citation rate.

Table 6. Author keywords specific to publications of not in Q1 journals with high average normalized citation rate.

label	cluster	Occurrences	Avg.pub.year	Avg.norm.citations
sensor network	2	4	2022	1.5679
financial technology	3	3	2022.333	1.2948
healthcare	1	3	2023.667	1.244
systematic literature review	1	6	2023	1.2355
smart city	1	5	2022.6	1.2162
assembly	3	5	2022.2	1.2066
strategic management	1	3	2024	1.2048
smart grid	1	6	2022.667	1.2008
data mine	4	3	2022.667	1.1871
sustainable manufacture	1	3	2022.667	1.1855
digital economy	1	6	2022.667	1.1838
industry	1	4	2022.75	1.1755
biologicalisation	1	3	2023	1.1714
hybrid manufacture	3	3	2023	1.1714
lean production	1	3	2022.333	1.1618

The terms ‘strategic management’ and ‘energy efficiency’ are more frequent in new publications and have high citation rates.

The terms ‘sensor network’ and ‘financial technology’ are found in earlier papers, but have a good citation rate.

Diagrams Created with the Scimago Graphica

Figure 4.1. Network of Author keywords from the first cluster of Figure 4.

Figure 4.1. Network of Author keywords from the first cluster of Figure 4.

‘Systematic literature review’ and ‘strategic management’ can be an interesting topic. In [28], which is a systematic literature review, the term ‘strategic management’ appears extensively in the form of references to the journal: Analysis & Strategic Management.

Figure 4.2. Network of Author keywords from the second cluster of Figure 4.

Figure 4.2. Network of Author keywords from the second cluster of Figure 4.

In the topic of ‘sensor network’ and ‘aas’, the former term is highly cited and the latter term is more common in new publications. Context: “Asset Administration Shell (AAS) – a machine–readable representation of an asset in a digitalized production system” … “AAS can provide static information, such as the manufacturer, type or serial number, as well as dynamic data such as the current reading of a sensor” [29].

Figure 4.3. Network of Author keywords from the third cluster of Figure 4.

Figure 4.3. Network of Author keywords from the third cluster of Figure 4.

The second, red cluster in this figure refers to ‘machine leaning’ and ‘artificial intelligence’, with the term ‘financial technology’ appearing in articles with higher citations. Machine leaning algorithms are used in analyzing issues related to cryptocurrencies, an example of such work is the following [30].

Figure 4.4. Network of Author keywords from the fourth cluster of Figure 4.

Figure 4.4. Network of Author keywords from the fourth cluster of Figure 4.

The topic of the fourth cluster: ‘data mining’, ‘innovation management’ and ‘decision making’ is well covered in the works [31, 32].

Diagrams Created with the Scimago Graphica

Previously, when building a keyword network with Scimago Graphica, filters were used to set the threshold for the ‘total link strength’ and ‘cluster’ parameters. If the task is to analyze the topics of articles published in the most cited journals, it is advisable to introduce filtering by the ‘avg.norm.citation’ parameter. Figure 3.1cit–3.3–4cit show the results of Author keyword clustering for publications published in journals included in Q1, but unlike Figure 3.1–3.4 filtering was used: ‘total link strength’>20 and ‘avg.norm.citation’>1,1.

Figure 3.1cit. Network of Author keywords from the first cluster of Figure 3 when filtering ‘total link strength’>20 and ‘avg.norm.citation’>1,1.

Figure 3.1cit. Network of Author keywords from the first cluster of Figure 3 when filtering ‘total link strength’>20 and ‘avg.norm.citation’>1,1.

The topic ‘servitization’ and ‘digital economy’ can be seen as a contender for relevance. The publication [33] provides a context for these two terms in its title. Another example from this paper is the line: “the impact of the digital economy on the servitization of industrial structures in the eastern, central, and western regions”.

Definition: Servitization is a shift in manufacturing that enables the creation of innovative business models and revenue streams by incorporating value–added services into their products.

Figure 3.2cit. Network of Author keywords from the second cluster of Figure 3 when filtering ‘total link strength’>20 and ‘avg.norm.citation’>1,1.

Figure 3.2cit. Network of Author keywords from the second cluster of Figure 3 when filtering ‘total link strength’>20 and ‘avg.norm.citation’>1,1.

The theme of ‘industrial iot’ and ‘industry 5.0’ dominates in this chart. Strings “Emerging technologies such as blockchain and digital twins are essential for … Industry 5.0 … the growing number of industrial IoT nodes … enable secure transmission … difficult” from [34] reflect the context of the terms.

The keyword ‘industrial iot’ is an example of how it is not enough to simply replace individual keywords as abbreviations with their full names, it is also necessary to replace abbreviations in complex keywords, for example using the sed utility.

Figure 3.3–4cit. Network of Author keywords from the third and fourth cluster of Figure 3 when filtering ‘total link strength’>20 and ‘avg.norm.citation’>1,1.

Figure 3.3–4cit. Network of Author keywords from the third and fourth cluster of Figure 3 when filtering ‘total link strength’>20 and ‘avg.norm.citation’>1,1.

The three figures above show that high cited keywords form a clearly dominant cluster, as shown in the Figure 3.1cit.

The topic ‘3d concrete printing’ can be found in the review publications for 2024 [35, 36, 37].

Conclusions

References

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