3.2. Country Statistics
The analysis of the most productive countries was based on the associated country of the author. Table 4
gives a comparative analysis of the most active countries according to the Scopus, WoS, and merged databases.
The countries that have devoted the most effort in publishing are the USA, the People’s Republic of China, and the UK. Those countries are the three leading, most active countries in all three databases. Canada, Australia, Taiwan, and South Korea appear in all databases, which confirms their activity. Their ranking order is slightly different depending on the observed database. Differences in support of publications between Scopus and WoS are visible if we keep track of Iran, Malaysia, and Italy, which are among the top 10 active countries in the WoS database but are not among Scopus’ top 10. Equally, Brazil, Egypt, and India are on Scopus’ top 10 rankings and are not shown on WoS’s rankings.
The analysis of Table 4
shows that the authors of some countries prefer WoS rather than Scopus. WoS has a significantly larger number of publications compared to the Scopus database. Authors from the UK and USA have published twice as many publications in the WoS database than in the Scopus database. It can be noticed that authors from Canada and China publish significantly more papers in WoS than in Scopus. The number of published papers in WoS is over three-times higher. An even more significant difference relates to Australia, whose authors published 9 papers in the Scopus database and 48 in WoS database, over five-times more.
Of the top 10 countries, five were from Asia, two were from Europe, two were from North America, and one was from Oceania. Given that 24% of publications include international collaboration, it can be concluded that the topic is favorable for international cooperation.
shows the percentage of participation of individual countries in the total number of selected 750 publications. As expected, given the size, development, and the already known fact that they are world leaders in scientific production, the People’s Republic of China and the USA are definitely leading in publishing scientific papers on this topic. Each of the two countries participate with almost 20% in the publications among the selected 750 papers. The importance of their percentage comes to the fore when comparing a further list of states and their percentages of participation. The People’s Republic of China and the United States together account for 38.80% of the total number of publications. It is interesting to compare that the same percentage of participation has the sum of the following eight countries on the list—UK (8.53%), Australia (6.67%), Canada (6.40%), South Korea (4.0%), Malaysia (3.60%), Taiwan (3.60), Iran (3.20%), and Italy (3.20%). A more detailed list of all countries whose authors participated in the publications is given in Table A1
of Appendix A
. In addition, the total number of publications per country was calculated, and the ratio of their number of publications to the total of the 750 publications analyzed was given.
3.4. Journals Statistics
Publications are retrieved from a wide range of journals and different knowledge areas, totaling 297 journals and conferences. Table 7
lists the top 10 journals that published the most scientific papers on the topic. These turned out to be journals of extremely high quality. Eight out of ten are defined as the highest-ranked journals in a category (Q1), and just two out of ten are positioned as Q2. These journals are distributed in different knowledge areas such as engineering, energy, social science, environmental science, business, management and accounting, computer science, and decision sciences. The topic is interesting not only in the field of construction but also beyond. Obviously, this topic has widely attracted the attention of many researchers. This implies that this paper will not only have a scientific contribution for engineering and construction, but it is relevant to other management sciences for all researchers working with intelligent systems or those working on digitization, industry 4.0, etc.
One of the most commonly used journal evaluation measures is the impact factor. The journal may have a different value for this factor each year. This is the number of citations the journal received in the last full year for articles published in the previous two years, divided by the total number of articles published by the journal in the previous two years or simply the average number of citations published in the last two years.
By analyzing Table 7
and the impact factor, first on the scale with a factor of 9.297 would be the Journal of Cleaner Production and then Automation in Construction with 9.160. Although the impact factor is considered to be one of the most commonly used measures for evaluating a journal, some researchers have pointed to its negative characteristics and noted that these values should not be blindly monitored. Journal impact factors correlate poorly with the actual citations of individual articles and are not statistically representative of individual journal articles. In addition, this value has nothing to do with the assessing of the quality of individual articles, but rather journals [43
CiteScore measures the average number of citations per document that a title receives over a period of three years. It refers to articles, reviews, letters, notes, editorials, conferences, and other documents, which give a complete picture and a more comprehensive and up-to-date view of the journal’s impact. The transparent and straightforward calculation gives CiteScore a clear advantage over other measures [45
3.5. Author Statistics
By browsing the Scopus and WoS databases, it is possible to obtain specific information about each author. In addition to the variants of authors’ names and surnames used in publications (published names), a list of authors’ current and previous affiliations is visible. In addition to the total number of publications, there are papers on which author was guided as the first, last, or corresponding author.
Author statistics were made separately for Scopus, Web of Sciences, and merged databases. Certain illogicalities or errors were noticed during the analysis as the authors used different variants of their names and surnames. The error occurred because of the connection between different variants of names and affiliations, therefore the databases classified and led them as different authors. Pranckute emphasized [33
] that it is possible that, for example, the Scopus database can make a mistake in classifying the author due to the absence of an email address. Errors were corrected, therefore Table 8
contains the ranking list of the 10 most active authors.
Databases give us information on citing articles and times cited, which is extremely important to reveal the impact of an author’s scientific work, whether the topic is current, interesting, and whether some new data important for further research was discovered. Usually, it is possible to see the time progress of the number of publications in parallel with the line indicating the number of citations of authors by year. In this regard, it is important to emphasize the h-index. Although the h-index was introduced on the scientific scene in 2005, due to the simplicity of the calculation and its objectivity, it became a very popular, and monitored the index that measures the effect of productivity and the citations of authors’ publications. It quantifies the results of an individual’s scientific research [18
The h-index defines the number (h) of the best papers in a database, each of which is cited at least h times. One limitation is that the h-index may vary from database to database, as they cover different publications in different age ranges. Although initially the index was used only to measure the productivity and citations of authors, later the h-index began to be used to measure impact of journals, institutions, etc. [33
]. What is also considered to be a disadvantage is that few authors can have the same h-index even though one of them may have a significantly higher number of published papers and higher number of total citations. In this regard, the h-index is unfavorable for new journals and researchers [33
According to Hirsch [18
], it is good if an author in 20 years of research has an h-index of 20. It is outstanding if the h-index is 40 and truly exceptional if it is 60. Considering Table 9
and all the data collected from Scopus and WoS databases, Shen G.Q. and AbouRizk S. are considered to be outstanding. Taking into account Shens’ h-index of 57 from Scopus and 51 from WoS, he tends to reach the Hirsch category of truly exceptional soon. Zhang L.M. is now in the Hirsch category of good but believing that the author doesn’t have 20 years of research experience, it is to be expected that will soon move into higher categories.
In order to conduct a quality bibliometric analysis, it is very important to consider the cooperation of the authors in scientific papers. In this way, it is possible to see their personal development and the development and progress of a particular topic [33
]. A co-authorship analysis was made in VOSviewer, and the visualization is visible in Figure 5
. The network of cooperation has been developed, connecting different institutions and countries, which is further proof of the topicality of the topic.
lists the 10 most cited publications. In addition to information on the title, authors, the number of citations, the year of publication, the author’s affiliation, and the country associated with the author were also written. The most frequently cited paper is “A fuzzy decision framework for contractor selection” by Singh and Tiong from Singapore, published in 2005. It has been cited a total of 162 times. In second place is “Cognitive biases and decision support systems development (a design science approach)” by Arnott from Australia, published in 2006, which was cited a total of 150 times. What is somewhat surprising is the third publication in the row. In collaboration with American and Chinese authors (Ding, Zhou, and Akinci), the paper “Building Information Modeling (BIM) application framework: The process of expanding from 3D to computable nD” was published in 2014. The total number of citations is 149. Although published almost 10 years later, the number of citations is just slightly lower and can be expected soon to surpass the top two articles of this analysis. Moreover, the work published in 2018 climbed very high on the list of the most cited publications. In just three and a half years, it has become more frequently cited than scientific papers published more than a decade ago. It is the work of authors Li, Xue, Li, Hong, and Shen from Chinese universities entitled “An Internet of Things-enabled BIM platform for on-site assembly services in prefabricated construction”.
Among the affiliates, the institutions seen in Section 3.3
are repeated. Institutional statistics, i.e., the top active institutions—Huazhong University of Science & Technology, University of Hong Kong, and Hong Kong Polytechnic University.
Very interesting information lies in the analysis of the countries associated with the author. In addition to the People’s Republic of China, the USA, Australia, South Korea, and Singapore, which have already been mentioned in Section 3.2
or Section 3.3
, the list includes Serbia. Two scientific papers have been published at Serbian universities and are high on the list of the most cited papers. The article by Gigovic, Pamucar, Bozanic, and Ljubojevic, “Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia”, was published in 2017. Despite the recent year of publication, the paper was cited 141 times and occupied the fifth position on this scale. Immediately following this, on the sixth rank position, is the article by Rikalovic, Cosic, and Lazarevic, “GIS Based Multi-Criteria Analysis for Industrial Site Selection”, published in 2014, cited 140 times.
3.6. Research Hotspots and Evolutions
Keywords are indicators of studies that transfer main topics. Keywords that appear together can be identified and analyzed to reflect the most interesting research issues in a particular field. For this article, the visualization of the most common words was made in VOSviewer. The document imported into VOSviewer had to be created as a .ris file, since the data were not imported directly from Scopus or WoS but were edited, overlapped, and had duplicate publications removed. In order not to show forest data and all 4184 different keywords, the minimum number of occurrences of a keyword was chosen to be ten. This reduced the range of keywords to 103 words. A list of all terms above the threshold is shown in Appendix A
) along with occurrence data. The created visualization (Figure 6
) shows 103 nodes divided into four clusters. The clusters are very intertwined, and the total link strength is 7240. Every keyword is considered to be an item that can have various attributes. One of them is weight, an attribute that is restricted to non-negative values. Weight indicates the importance of the item and, if the item is more important, it will be more visible and noticeable on visualization. There are two standard weight attributes, referred to as the Links attribute and the Total link strength attribute. Table 11
shows the top 20 most common and strongest words of this research. These are the words that are most prominent in the visualization [46
By counting the appearance frequency of keywords, hot spots of the research topic can be analyzed [47
Cluster number 1 is red (Figure 6
). It is the most significant cluster with 36 keywords. All terms in this cluster indicate the system development and digitalization of the AEC industry, as can be concluded from the terms: “BIM”, “critical success factors”, “decision support”, “GIS”, “impact”, “implementation”, “industry”, “information”, “knowledge”, “management”, “methodology”, “performance”, “simulation”, “sustainability”, and “quality”. Although the bibliometric analysis was made with publications from all over the world, it is interesting that “Hong Kong” appears as one of the keywords of this cluster. Given that the three Hong Kong universities are among the top 10 most active institutions, this information is consistent and only confirms their strong influence. By reviewing these clusters, we can conclude that the three universities published the most on the topic of the progress and improvement of digitalization in the AEC world.
Digitization is seen as a dynamic process of change driven by the rapid development of innovative concepts that brings significant potential benefits to the construction industry. The literature often states that we live in the age of Industry 4.0. It is the age of information technology, digitalization, and the emergence of machines driven by artificial intelligence. Artificial intelligence is the replacement of work processes intended for humans by automated or semi-automated machines, commonly referred to as “intelligent agents”. This reduces the chance of errors in work processes. Intelligent agents perceive the environment and increase the chances of success [48
]. Although construction is one of the largest industries in the world, digitalization is being introduced very slowly among construction companies. Moreover, construction is considered to be one of the least digitized industries in the world. A significant shift in digitalization has been achieved with the introduction of building information modeling (BIM) [48
]. However, for this research area, there is a great need for more studies on the organizational changes necessary for digitization and how to evaluate and implement digital technologies to support business on the construction site.
Cluster number 2 is green (Figure 6
) and brings together 29 items, among which are “automation”, “benchmarking”, “computer simulation”, “construction equipment”, “contractors”, “costs”, “decision making”, “efficiency”, “KPI”, “investments”, “project management”, “project performance”, “risk analysis”“, and “success”. The common denominator of the whole cluster is the increasing long-term productivity of construction projects.
To create a competitive advantage in the construction industry, it is necessary to increase productivity, develop construction equipment, and automate work processes. It is increasingly common to think that knowledge management is no less important than a key organizational ability of construction company managers. Benchmarking is a great approach to continuously improve and advance the construction company’s processes while taking into account competitive activities and dynamics. It provides a systematic framework for identifying, classifying, and evaluating enterprise processes, activities, and performance. The primary goal of benchmarking is continuous improvement while monitoring the activities of other competing companies [50
]. There are three main types of benchmarking: (1) internal—research and analysis of practice within a company’s department to seek progress; (2) competitive—research of competitive practices and implementation in its business; (3) generic—research on the best practices of a company that does not operate in the same type of industry. Competitive benchmarking is best researched through the literature. However, there is still a lot of unexplored area regarding benchmarking and construction companies [51
Cluster number 3 is blue (Figure 6
) and contains 23 items such as: “algorithms”, “artificial intelligence”, “classification”, “data mining”, “DSS”, “decision theory”, “decision threes”, “forecasting”, “fuzzy logic”, “mathematical models”, “neural networks”, “optimization”, “planning”, “prediction”, and “scheduling”. This cluster focuses on the optimization of construction projects using artificial intelligence. Every construction company in the form of its progress must certainly work on process optimization. It cannot be competitive, thrive, and break into new markets if it does not pay attention to this aspect. Optimization can increase resource efficiency, reduce construction time, minimize construction costs, etc.
Cluster number 4 is yellow (Figure 6
) and has 15 terms. The main keywords of this cluster are: “analytic hierarchy process”, “case-based reasoning”, “contractor selection”, “criteria”, “decision-making”, “model”, “supply chain management”, “support”, and “uncertainty”. A common notion of these terms is multicriteria decision-making.
Comparing Figure 6
with the network visualization of clustered keywords and Figure 7
with the presentation of keyword usage over the years, it can be seen that the terms of clusters no. 1 and no. 4 are quite new. Researchers have been using them frequently for the last 5 years and that is why they are colored yellow or very light green on Figure 7
. Especially new are considered to be: “BIM”, “impact”, “integration”, “support-system” and “decision-support” from the red cluster (no. 1), and “decision-making”, “uncertainty” and “criteria” from the yellow cluster (no. 4). There are no blue or dark green items in this area. This visualization confirms that the concepts of cluster no. 1 (red) and cluster no. 4 (yellow) still have a lot of room for improvement and research. The topics are broad and modern. This can be used by future researchers to direct their research topics or develop their current topics in this direction.
The right side of Figure 7
, which includes mostly cluster no. 2 (green) and cluster no. 3 (blue), according to Figure 6
, is darker in color. The blue and dark green colors suggest that researchers used them ten years ago or even before. The size of the node indicates the frequency of use of the term. Therefore, the “decision support system” is the biggest node of this research, the most commonly used, and one that researchers have often written about. Ten years ago, the terms “construction industry”, “artificial intelligence”, “project management”, “decision making”, “risk management”, and “mathematical models” were also very popular. Some of the newer and more modern terms from these clusters are “big data” from cluster no. 2 and “neural network” and “classification” from cluster no. 3.