Next Article in Journal
Evaluation of Biogas and Solar Energy Coupling on Phase-Change Energy-Storage Heating Systems: Optimization of Supply and Demand Coordination
Next Article in Special Issue
‘Buildability’ in the Digital Age: A Phenomenological Discourse of Industry Practitioners’ Perceptions
Previous Article in Journal
Deformation Monitoring of Monopole Communication Towers Based on Multi-Source Data Fusion
Previous Article in Special Issue
Overcoming Head Contractor Barriers to Sustainable Waste Management Solutions in the Australian Construction Industry
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 13
Issue 11
10.3390/buildings13112711
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Review of the Digital Skills Needed in the Construction Industry: Towards a Taxonomy of Skills

by
Fida Hussain Siddiqui
1,*,
Muhammad Jamaluddin Thaheem
2 and
Amir Abdekhodaee
3
1
Department of Civil and Construction Engineering, School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
2
School of Architecture and Built Environment, Deakin University, Geelong, VIC 3220, Australia
3
Department of Mechanical Engineering and Product Design Engineering, School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
*
Author to whom correspondence should be addressed.
Buildings 2023, 13(11), 2711; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13112711
Submission received: 3 August 2023 / Revised: 20 October 2023 / Accepted: 24 October 2023 / Published: 27 October 2023
(This article belongs to the Special Issue Selected Papers from the 45th Australasian Universities Building Education Association (AUBEA 2022))
Browse Figures
Versions Notes

Abstract

:
The construction industry is slowly embracing digitalisation in line with the Industry 4.0 revolution and the aftermath of the COVID-19 pandemic. However, progress has been sluggish due to stakeholders’ limited awareness of digital skills. This study addresses this issue by developing a comprehensive taxonomy of digital skills required to successfully implement the Industry 4.0 principles of digitalisation in the construction industry. A systematic literature review was conducted by mining the Scopus and Web of Science databases to identify relevant literature and map the skills currently used or needed for digitalisation. The study also examined publication trends and outlets to gain insight into developments. Additionally, VOSviewer was used to conduct a scientometric analysis of the shortlisted articles to identify important keywords and authorship collaboration networks within this research domain. A total of thirty-five digital skills were identified from the literature. These skills were organised into a taxonomy with categories named automation and robotics, coding and programming, design, drafting and engineering, digital data acquisition and integration, digital literacy, digitisation and virtualisation, modelling and simulation, and planning and estimation. The developed taxonomy will help stakeholders plan strategically to provide digital skills to the new graduates joining the workforce, enabling a more comprehensive approach to the digitalisation of the construction industry.

1. Introduction

Digitalisation involves converting the existing manual processes into automated, self-regulated digital processes using information and communication technology (ICT) tools, techniques, and practices. However, digital technologies have a broad and diverse definition that can vary depending on an individual’s needs, situation, and relationship with the technology. Therefore, digital technologies, for instance, building information modelling (BIM), augmented reality (AR), and virtual reality (VR), may mean different things to different people. Moreover, these technologies, which consist of hardware and software, can serve multiple purposes and be utilised throughout various phases of a construction project [1].
Several industries, including manufacturing, retail, and banking, have recognised the benefits of digitalisation [2]. However, the construction industry has yet to adopt it and reap its usefulness fully [3,4,5], even in developed countries such as Australia [6]. The globally existing and fast-paced digitalisation in the context of Industry 4.0 or the digital revolution [5] urges the construction industry to transform rationally for efficient performance [7]. Disruptive changes have occurred in the construction sector, starting with transitioning from manual to computer-aided design (CAD) and then to BIM. Other digital technologies, including the Internet of Things (IoT), AR, VR, artificial intelligence (AI), drones, laser scanning, 3D printing, big data analytics, geographic information systems (GIS), and robotics, are the applications of the Industry 4.0 concepts [8,9,10,11,12,13,14,15]. These technologies help further achieve modern-era sustainable solutions such as a circular economy within the construction supply chain [16,17]. However, they have limited and slower adoption in the construction sector [18,19]. Nevertheless, these technologies help eliminate many of the inefficiencies of complex construction projects [18], improving the performance of the construction projects [13], such as safety and quality [20].
Researchers report that adopting digital technologies and the transition towards Industry 4.0 is hindered by a lack of skills, knowledge, expertise, and experience [6,18,21,22,23]. These barriers affect the individual’s and firm’s performance [14]. This claim is backed by research that indicated that roughly 7.5% of time loss occurs due to malfunctioning of the ICT devices because of a lack of ICT skills among the workers [24]. Furthermore, Francis and Paton-Cole [25] emphasised the Victorian government’s findings that approximately 75% of construction industry employers believe technical and job-specific skills are lacking in the industry [26], which affects project costs and productivity [27]. Several other authors, such as Becker et al. [28] and Djumalieva and Sleeman [29], also point out that digital skills are and will be required for most jobs. Suprun et al. [18] reported that the existing skills gap might appear more significant soon as digital technologies and relevant skills needed keep evolving. To this point in time, there is an overwhelming demand for digital skills in the labour market [30]. Hence, enhanced skill sets should be provided to the site personnel and higher management [31] to manage the challenges faced by Industry 4.0 in the construction industry [7].
The research and application landscapes are changing in the construction industry domain, for example, Industry 4.0 applications [32,33], digitalisation, and the utility of AI for innovation in construction firms [34]. The evolution of information technology (IT) related applications ranges from generic internet and email access to architecture, engineering, and construction (AEC) specific applications such as foundational design and code compliance checking [35,36]. Relevant emerging technologies are adapted in almost all the project lifecycle phases and add value to the projects [37]. Implementing IT-based systems in a construction organisation faces risk factors such as time limitations and lack of training; however, it could be managed by maintaining dedicated IT professionals on the project [38]. Still, on average, AEC firms invest less in innovation and give it less significance than their counterparts in advanced industries such as IT and electronics [39]. It is evident from past research [40] that dedicated developers work on software development applicable to various domains, including the construction industry. However, the developed framework consists of cyclic efforts to reach a consensus to design the intended application outcome [40].
Nonetheless, the abovementioned IT advancements have enabled computing to become an increasingly vital component within AEC disciplines [41,42] and, consequently, have pushed the construction industry stakeholders to improve their state of innovation and automation through indigenous human resources [43,44]. As a result, more digitalisation and automation skills are deemed necessary and taught through formal and informal training to construction professionals, making this a development field. It allows the construction industry to develop technological tools that are better suited to the construction industry [45]. In this regard, the increasing number of publications on digitalisation-related topics in recent years attests to researchers’ growing interest in the subject, indicating that such topics contribute significantly to the construction industry’s worldwide development [46]. With the practical application of Construction 4.0 technologies and practices, including BIM, AR, and VR, the relevant requirement for a new set of skills within the sector’s human resources is also evolving [6,47]. However, at the same time, this evolved skillset requirement is a challenge for the industry, academia, and government [5,33,48]. This shift towards digitalisation and the pressing need for relevant skills is further evident through grey literature [49,50,51]. As a result, computing and programming skills are being increasingly considered and taught to upcoming civil and construction graduates [52,53].
These arguments show that the construction industry’s skills requirements have emerged and evolved. Stakeholders have continuously tried to assess the workforce skills requirements to acknowledge the criticality of issues due to the skills shortage and propose and implement skills development strategies and practices [54,55]. It presents the further need for an updated evaluation of digital skills. Also, it will be evident from the subsequent sections that various digital skills are available in the literature but in an isolated form. An effective categorisation of the identified digital skills has been missing. In the absence, the relevant academic and industry stakeholders struggle to target the training and upskilling of the workforce fur future needs. Consequently, this research aims to synthesise the state of the literature on the digital skills currently used or needed across a range of job roles and professions, including design, estimation, planning, and scheduling, to name a few, in the broader construction industry domain and to develop the taxonomy of digital skills per the construction industry needs. This taxonomy will help academia and industry to focus on the presently demanded digital skills.

2. Methodology

This study utilised scientometric analysis and a systematic literature review (SLR) methodology to synthesise the literature’s state and develop the digital skills taxonomy, as presented in the flowchart (Figure 1). Scientometric analysis involves quantitative and qualitative methods to analyse the structure, evolution, and impact of scientific knowledge [56]. In this study, the scientometric method was used to examine the publication patterns, trends and outlets in the field of construction management, as covered by several authors [57,58,59]. The data collected were further analysed to identify the most frequently studied topics and authors and map the relationships between different scientific fields and authors, i.e., co-occurrence and co-authorship networks. VOSviewer was used for this purpose.
VOSviewer is a freely available statistical tool for measuring the impact of research through bibliometric analysis and has been successfully applied across various academic disciplines. It offers basic functionalities for producing, visualising and representing scientometric networks [60]. Specifically, VOSviewer uses a graphical representation to visualise the correlation strength between nodes, with warmer colours indicating a higher or stronger correlation strength [61]. Furthermore, the visual interpretation of the relevant literature with VOSviewer can identify emergent common themes and relationships between their elements. In the construction management discipline, VOSviewer has been used successfully to analyse and visualise keyword mapping, author collaboration networks, prominent outlet mapping, country collaboration networks, and research clusters.
Conversely, SLR uses replicable methods to identify, screen, and evaluate the studies undertaken in the research area [62]. Furthermore, as an enormous amount of research is produced for each research area, delineating a fine line between what is done and the possible research gaps becomes necessary. SLR can be utilised to comprehensively collate the existing works for a particular research question or aim [63,64,65]. The SLR procedure usually comprises the following stages: scoping, planning, searching, screening, eligibility, research syntheses, and presentation of results [63].
In the scoping and planning phases, the research focus statement was formulated, i.e., to develop a taxonomy (viz grouping, classification or categorisation) of the digital skills currently needed or utilised in the construction industry. Taxonomy in the scope of this research includes categorising skills and competencies, similar to previous studies [66,67], where the taxonomies were developed for standard soft skills and project management competencies. Based on this research theme, search keywords were brainstormed (based on a preliminary and non-systematic review of literature) and grouped under the categories “Construction Industry (C)”, “Digital Skills (DS)”, “Digitalisation (D)”, “Systematic Literature Review (SLR)”, “Taxonomy (T)”, “Education (E)”, and “Stakeholders (S)”. The “C” group included the keywords AEC, architectural engineering, civil/construction engineering, construction engineering and management, and construction industry/management/sector. The “DS” group included keywords such as digital skill/literacy/competence, emerging technological skill/competence, digital competence, and technology/construction 4.0 skills. The “D” group comprises digital technology/transformation, emerging technology, Industry 4.0/4th industrial revolution, and advanced construction technology. The “SLR” group consisted of content/bibliometric/meta-analysis, systematic literature review, scientometric analysis, and text mining. The “T” group contained keywords such as classification, list, and group. The other groups—“E” and “S”—consisted of keywords (phrases) with nouns from the “C” group but with the addition of words such as classroom/curricula/education/program and student/graduate/professional, respectively. The preliminary inclusion criteria were set to consider only those publications that mention the digital competencies, roles or skills related to digitalisation of the construction industry utilised or needed within the architecture, construction engineering and management industries.
Several keyword combinations, for example, “C” and “DS”, “DS” and “S”, “DS” and “T”, and “S”, as presented in Appendix A, were used to search in the Scopus and Web of Science (WoS) databases, utilising title, abstract, and keyword search criteria. The Boolean operator “AND” was used between different keyword groups, while “OR” was used to control the scope within each group. While conducting the literature search, no year limit was specified in the search criteria, similar to a previous recent study [68], to include as many articles as possible. It was done to ensure a comprehensive and inclusive approach. A total of 471 records were found. After downloading the relevant records from the two databases into the MS Excel format, the records were merged, duplicates (353) were removed, and non-English language records (5) were discarded. With the title and abstract screening process, 34 articles were discarded.
Furthermore, in the detailed screening phase of the SLR, while sifting through the full versions of the 79 articles, 45 records were discarded based on the explicit inclusion and exclusion criteria. Either the articles did not consider the “digital skills” related discussion, did not mention the need or current utilisation of the research-themed skills, were related to only teaching and learning, or the full texts were unavailable. Also, during this screening phase, the scope was not specific to developing or developed countries. Therefore, articles from developed and developing countries were included to reflect the relevant literature on digital skills. Later on, the eligible records were scanned again. Through the snowballing method, which is to look at the 34 shortlisted publications’ references and citations, a further 12 research publications were found to be relevant. Hence, these were included, totalling 46 articles for the final synthesis. The shortlisted articles were published between 2007 and 2023 (to date). Figure 2 summarises the above steps in the form of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model.
Based on the mentioned criteria, the discarded articles consisted of irrelevant research such as small- and medium-sized enterprises’ sustainability approaches and relevant educational interventions [65], digital educational tools, personalised learning, and relevant assessments of the students [69]. Furthermore, a research work [70] that focused only on the professional and pedagogical competence development of the teachers at civil engineering universities was also removed. In other research [71], road construction site managers’ competencies were assessed to identify the failures, and construction management system, project administration, and resource procurement competency factors were emphasised to be improved, so it was removed as well. Moreover, human resource development strategies were ascertained in research work but with a keen focus on soft skills rather than being digital precisely [72], so they were not aligned with the scope and hence were discarded as well. Though the mentioned research works fall under the larger domain of civil engineering works and discuss the needed competencies, they still lack the targeted focus outlined in this work’s scope. It shall be noted that if the keywords “DS” or “D” were to be searched in combination with “C”, it would result in enormous results. However, the focus was not on the overall digitalisation in the construction industry domain or the developed digital tools and technologies. Hence, the relevant search criteria were limited to the need for or utilisation of digital skills. Therefore, all the papers not falling within this scope were removed. Furthermore, it shall be noted that it is possible to search different digital technologies or concepts, such as BIM, AR, and VR, along with the keyword “DS”. However, it would yield a massive number of search results and eventually might not lead to shortlisting any other digital skill because of the already-considered keywords related to digitalisation.

3. Results and Discussion

The construction industry is undergoing the process of global digitalisation. Technological changes in the construction industry help improve the processes and tools on-site and in the design and project offices to manage projects during various lifecycle phases [1]. The possibilities are limitless, including automation of construction sites [73], digitisation of design documents, utilisation of big data for enormous data fetching and management processes, and many more. The need for relevant digital skills has also intensified due to the increasing utilisation of these and many other digital technologies in the construction sector [23]. Any ability that involves the computer and the internet can be broadly termed under the umbrella of “digital skills” [74]. They combine digital mindset, knowledge, competence, skill, and attitude [75]. Engineers with digital skills are expected to be more productive and beneficial for organisations [15].

3.1. Publication Trends

From analysing the considered publications, as presented in Figure 3, the results show an equal number of conference proceedings and journal articles, with 22 publications each. In addition, there are two book chapters in the dataset.
Furthermore, the trend shown in Figure 4 points to a variation in the number of annual conference and journal articles published. Initially, a conference paper trend was observed until the year 2021, whereas, from 2019, journal publications were also evident. The year 2021 saw the highest number of publications (8 altogether). Specifically, the highest number of conference publications (4) was found in 2020, while for journal articles, the highest number (7) was in 2022. The years 2014, 2019, and 2021 observed three conference publications each year.
On the other hand, in 2021 and 2023, five publications each were noted as journal articles. In 2020 and 2023, there were relevant publications in the form of a book chapter, the only ones found in the current dataset. It is realised from the results that conference publications specifically saw two peaks: in 2014 and 2020. In comparison, journal articles were more frequent in the later years. One of the possible interpretations could be the urge of researchers to present their works with limited supporting literature to the appropriate peers and audience in the form of a conference and gain early feedback for improvement [76,77]. It also provides quicker dissemination of relevant knowledge as the turnaround time of conference papers is significantly lower than those published in academic journals. Though the conference papers are easier to publish, they are still peer-reviewed by experts in the field, providing quality control. However, this further suggests that future researchers in this area could also consider publishing in academic journals to achieve a wider reach and rigorous critique for better quality and outcome.

3.2. Publication Outlets

Table 1 presents the distribution of the publications in the respective academic outlets. The results show that the authors of the articles have published in a diverse range of outlets, including both conference proceedings and academic journals. The authors have contributed significantly to the body of knowledge in their field by disseminating their research through various outlets. Among the conference publications, the American Society for Engineering Education (ASEE) Annual Conference & Exposition was the most popular outlet, with four publications. The Royal Institution of Chartered Surveyors (RICS) Construction and Building Research Conference, IEEE International Conference on Emerging eLearning Technologies and Applications, and International Conference of Education, Research and Innovation had two publications each. Whereas for the journal articles, the journal ‘Buildings’ had the most (3) publications. After that, the journals ‘Journal of Construction in Developing Countries’, ‘International Journal of Construction Management’, and ‘Journal of Management in Engineering’ observed two publications. The remaining journals and conference outlets had one publication each in the considered dataset. It points to the lack of interest in mainstream construction digitalisation journals, for example, Automation in Construction, in publishing education and skills-related research on construction digitalisation because of the limitation of their scope. It involves using ICT in design, engineering, and construction technologies and maintaining and managing the built environment [78].
Similarly, the Journal of Computing in Civil Engineering focuses on innovative and novel ideas in computing applicable to the engineering profession. These may include innovations in artificial intelligence, parallel processing, distributed computing, graphics and imaging, and IT [79]. So, this limitation practically directs the research published in such journals towards technology innovation and application. However, to improve the academic aspects of skill development, these outlets may broaden their scope to accommodate high-quality research on digital skills for a much more extensive reach rather than limiting to solution development or application-oriented research.
Besides that, there are other significant publication outlets for researchers in the construction education area, which could be aimed at publishing research related to the need for relevant skills. For example, the Journal of Civil Engineering Education and the International Journal of Construction Education and Research are some of the many outlets. The Journal of Civil Engineering Education focuses on research related to effective methods to teach civil engineering principles and prepare students through formal education, teaching practice issues, ethics education, case studies of pedagogy, and lessons learned that are unique to the civil engineering practice [80]. Similarly, the International Journal of Construction Education and Research contributes to understanding issues and topics associated with construction education and the construction industry. The journal’s scope also embraces workforce development and pedagogical content [81].

3.3. Co-Authorship Networks

The scientometric analysis investigated the authors’ co-authorship networks. Awareness of collaborative teams and authors in any research area boosts the effectiveness and efficiency of scholarly works [82]. Glänzel and Schubert [83] reasoned that the established networks of authors help publish the articles in good outlets, resulting in more citations. Thus, a co-authorship network is generated via VOSviewer. The type of analysis chosen was co-authorship. The unit of analysis was set to ‘authors’ and the counting method to ‘fractional counting’ to select the top investigators. An author’s minimum number of documents was set to one to get an overall view. There were 136 authors altogether. When processed for analysing, two networks were generated. One consists of all the authors (Figure 5), whether interconnected or not; the second consists of seven items (Figure 6) when the software tool prompts for the set of connected items only.
Figure 5 shows the network map of 136 authors in 42 clusters, the most extensive of which is seven authors. Most authors are not interconnected, except for being co-author of each other in the same publication. It depicts that not much of a research network is currently established in the research race to present the digital skills required for the construction industry. One prospective explanation could be that the authors focus on and assess the lack of digitalisation or relevant need for skills limited only to their institutes, companies, or regions. Another possible reason for this lesser collaboration is that this research area requires interdisciplinary collaboration. The research on digital skills demands that researchers of traditional fields such as civil engineering, construction and architecture join hands with modern fields, for example, ICT, under the stewardship of researchers of education and training. Understandably, interdisciplinary collaboration comes with logistic challenges, such as identifying and connecting with potential collaborators, establishing working relationships, demonstrating mutual value, and traversing through sector-specific jargon. Since researchers tend to work in silos, achieving this interdisciplinary collaboration is more challenging than perceived. While several researchers work on digital construction topics, digital skills and their provision to young graduates see less action, mainly due to the need for three research dimensions: AEC, ICT, and education.
Figure 6 presents that only seven authors are interconnected with each other. Mandicak T, Behun M, and Mesaros P are the most prominent authors. They are connected with six other authors. The total link strength for each mentioned author is 3, meaning they have co-authored three documents. Spisakova M and Kanalikova A, are found to be co-authors of each other along with the three prominent ones, but in only one publication. In contrast, Behunova A and Mesarosova A are co-authors with the previously mentioned three prominent authors through one document each but without sharing the same document authorship with each other. On investigating their relevant articles, it is realised that they have worked on the research areas of digital competencies, including BIM, amongst the construction project management stakeholders [84,85,86].
Furthermore, Mandicak T, Mesaros P, and Spisakova M are affiliated with the Department of Economics, Management and Information Systems in Construction, Faculty of Civil Engineering, Technical University of Košice, Košice, Slovakia. In contrast, Kanalikova A is with the Department of Applied Mathematics and Descriptive Geometry at the same university. At the same time, Behun M and Behunova A are affiliated with the same university but with the Institute of Earth Resources, Faculty of Mining, Ecology, Process Control and Geotechnologies and the Department of Industrial Engineering and Informatics, respectively. Mesarosova A, on the other hand, belongs to the Department of Audiovisual Communication, Documentation and History of Art, Polytechnic University of Valencia, Spain. From the connections, it is evident that the collaboration mainly remained amongst different researchers within the multiple departments of the same university and country, except for an author from a Spanish university.

3.4. Co-Occurrence Network

Keywords highlight the foundational concept in an article and provide a way to figure out the main knowledge areas within a particular research domain [87,88]. Following the opted methodology, the shortlisted documents were imported into VOSviewer to identify the main keywords or clusters. The type of analysis chosen was ‘co-occurrence’. The unit of analysis was set to ‘keywords’ and the counting method to ‘fractional counting’. The minimum number of occurrences of a keyword was set to two. If only one was chosen, too broad concepts (in terms of keywords) would be highlighted, which may not reveal much meaningful analysis. Similar words or synonyms were also merged, such as “BIM”, “building information modelling”, or “building information modeling”.
There were 118 keywords altogether. With the condition of two occurrences of keywords, only twelve met the threshold. The resulting network is presented in Figure 7, which shows twelve items formed into 4 clusters with 21 links. The first cluster consists of the keywords: “BIM”, “BIM adoption”, and “BIM barriers”. Another set involves “Competence”, “Construction professionals”, and “Digital literacy”. After that, cluster 3 consists of “Construction industry”, “Artificial intelligence”, and “Skills”, and the last set consists of keywords “Construction management students”, “skill gaps”, and “training needs”. “Construction industry” and “BIM” are the central keywords here, with seven links each. The “Construction industry” keyword centralises around the need of construction stakeholders to improve their existing skills and competence set to modern technologies such as BIM and AI. It is also realised from this network diagram how the concept of BIM is quite central to most of the other relevant ideas, be it the adoption of technology and barriers in the construction industry or the overall digital literacy improvement of the relevant stakeholders. BIM is also interconnected with the “Skills” and “training needs” keywords, emphasising the importance of this skill for construction industry professionals.

3.5. Taxonomy of Digital Skills

In the literature, different terminologies have been used to refer to a particular digital skill. Hence, grouping or categorising these skills is done in this research for easy understanding. However, it is essential to note that numerous categorisations could be possible. Even overlapping is likely, based on a researcher’s approach and the scope of the study, i.e., a few of the digital skills in this study might be in a different category than the original category in the selected literature. In addition, a generic and broad term can be used for almost all the mentioned skills, i.e., “digital technological skills”.
Nonetheless, Table 2 presents the list of digital skills and categories. The second column consists of categories: automation and robotics; coding and programming; design, drafting, and engineering; digital data acquisition and integration; digital literacy; digitisation and virtualisation; modelling and simulation; and planning and estimation. These categories were formed partially based on inspiration from [14,31] and authors’ brainstorming sessions [89]. Finally, the third column consists of digital skills and is titled “Skills related to the use of” as it enlists various digital technologies, concepts, and software. Furthermore, the following section discusses individual categories and the relevant digital skills presented in Table 2, focusing on their current widespread utility and application in the larger construction industry domain. The discussion further directs towards the practical implication that digital skills must be well considered and comprehended for the fast-paced digitalisation of the construction sector.

3.5.1. Automation and Robotics

Growing advancements in the sector recently have increased the usage of several tools and methodologies such as 3D printing, automation-based technologies, autonomous vehicles, offsite construction and manufacturing, and drones/unmanned aerial vehicles (UAVs) [23,90,91,92]. For instance, UAVs have become increasingly popular in the construction industry for safely capturing data and generating 3D maps of construction sites [93]. With high-resolution cameras, these drones can quickly and efficiently capture images of construction sites from various angles and heights. The data collected can then be used to create accurate and detailed 3D maps of the site, which can be helpful for project planning, site analysis, and communication with stakeholders [73]. In addition, another digital technology, 3D printing, is becoming increasingly important in the construction industry as it has the potential to positively influence the industry by providing benefits such as fast construction, reduced material waste, less labour-intensive requirements, and improved worksite safety, as comprehensively summarised by Hossain et al. [91].
Furthermore, integrating automation and robotics technologies in the construction sector has improved cost, safety, quality and productivity [5,94]. New roles and responsibilities are also established whenever modern technologies are introduced in any industry. Robotics and automation in construction will create new opportunities and roles, specifically during the transition phase of human–machine interaction. Gerbert et al. [95] claim that new job positions will be more digital. For example, digital fabricator, digital coordinator, digital manager, and digital programmer will be a few of the latest roles [5]. The emphasis is on the fast-approaching digital construction era in which digital technologies and the need for digital skills are evident [18].
Table 2. Categorisation of skills.
Table 2. Categorisation of skills.
S. NoCategorySkills Related to the Use ofReference
1Automation and Robotics3D printing[23,73]
2Automation-based technologies[73]
3Autonomous vehicles[23]
4Digital fabrication[5]
5Managing and coordinating digital fabrication
6Drones/UAVs[23,73,93]
7Offsite construction and manufacturing[23]
8Robotics[23,73]
9Coding and ProgrammingAI[23,73,96,97,98]
10Computer programming techniques[18,22,73]
11Digital fabrication programming[5]
12Machine learning[23,73]
13Design, Drafting, and Engineering AutoCAD[5,22,99]
14Nanotechnologies[100]
15Structural design/software systems designing technical solutions[22,101]
16Digital Data Acquisition and IntegrationIoT[23,73]
17Smart sensors
18IT/ICT/computer information systems[99,101,102,103,104,105,106,107,108,109,110]
19Smart wearables[23]
20Digital LiteracyComputational tools/techniques; computer skills; Microsoft Office; construction software usage; awareness of and knowledge to use state-of-the-art construction technologies[22,85,93,99,101,102,103,104,105,111,112,113,114,115,116,117,118]
21Digitisation and VirtualisationBig data[18,23,97,103]
22Blockchain[23,97]
23Cloud computing and collaboration
24Data analytics[18,23]
25Data driven digitalisation[22]
26GIS[18]
27Laser scanning[23]
28Lidar survey scanner
29Modelling and SimulationBIM design and modelling[5,18,21,22,23,85,86,101,102,103,104,118,119,120,121,122,123,124,125,126,127,128]
30Digital twin[23,129]
31MR/AR/VR[23,73,97,103,130]
32Revit[99]
33Simulation[131]
34Planning and EstimationProductivity planning apps/software[23]
35Scheduling and cost estimating/management via technology and software, e.g., Navisworks[22,84,99,104,115,118]

3.5.2. Coding and Programming

The machine learning algorithms and AI concepts are mainly based on programming skills. Various benefits of programming skills can be ascertained through the literature. Kaiafa and Chassiakos [132] developed a comprehensive model in MS Excel for achieving optimal solutions to multi-objective resource-constrained project scheduling problems. The model used Visual Basic for an application-programmed genetic algorithm and aimed to minimise additional costs due to resource overallocation and day-to-day fluctuations. In addition, digital fabrication programming skills are required when robotic systems are designed for an autonomous construction industry [5]. Furthermore, BIM-based formwork and cladding quantity take-off were performed through a visual programming tool, Dynamo [133]. Also, AI’s implementation areas in the construction industry were collated by Darko et al. [134], such as modelling, forecasting, simulation, and decision-making. Similarly, existing AI implementation and its benefits were assessed in the UK’s construction industry. It was recommended that organisations consider AI’s implementation in the future to become competitive [98].
Furthermore, developing construction industries such as South Africa were also assessed regarding AI’s capability and uses. Though it was still lagging, it is highly recommended that construction organisations strategically design policies for skills and competencies development [96]. However, construction students are rarely introduced to such advanced developed curricula [23,73]; hence, the literature emphasises the need for digital skills related to programming languages for future employment [22,135,136]. Such digital skills help implement digital technologies, optimising the construction process [18].

3.5.3. Design, Drafting, and Engineering

This category of digital skills encompasses the skills related to using CAD software, design-oriented software and other pioneering engineering material development. Knowledge and expertise in CAD and drawing software, such as AutoCAD and Revit, can be beneficial for construction personnel [5,22], such as field managers [99], to handle drawing-related complexities. Designing tools and software [101], leading to technical solutions, has also been deemed necessary in the construction industry [22]. Furthermore, engineering and designing modern materials, including nanomaterials and their relevant applications, support the building construction sector [100]. Such innovative and new construction materials help increase the sustainability and efficiency of the construction processes [137]. Possessing these skills has been linked with the digital literacy of civil engineering graduates and is the expectation of employers, such as in Indonesia [22].

3.5.4. Digital Data Acquisition and Integration

Today, numerous tools and devices are linked to IT, ICT and IoT systems, such as radio-frequency identification, smart sensors and wearables, which help in the data acquisition and communication of essential information amongst different systems [92,104,138,139,140]. These systems help develop integrated, intelligent, innovative systems such as smart homes and cities [141,142]. Furthermore, digital communication and collaboration are stressed in the literature [143], such as by the Russian builders [101], for effective digitalisation processes in construction projects. However, the integration of IT, IoT and relevant systems has faced barriers such as managers’ lack of acceptability, limited skills, and lack of training and practical understanding [73,99,105]. It is further evident through the surveys conducted by past researchers [108,109], claiming that such skills are lacking among construction students and must be included in the curricula [106]. Hence, relevant digital skills such as IT skills [23,103], IoT skills [144], ICT skills [102], and skills related to the use of wearable technologies are deemed essential, leading to efficient safety monitoring [145] and better information circulation [107], organisational processes, and strategic planning [105].

3.5.5. Digital Literacy

Digital literacy is the knowledge and utilisation of digital devices for different tasks. The ongoing dynamic paradigm of digital technologies requires construction personnel to be digitally literate [101]; for example, field managers and personnel can interpret primavera schedules and Excel sheets [99]. Furthermore, digital literacy enables the stakeholders to use several computational tools and techniques, such as Microsoft Office in general and for resource calculation purposes, possessing an awareness of and essential knowledge to use state-of-the-art construction devices, technologies, and software [85,93,102,103,104,112].
Furthermore, the knowledge and competence of relevant essential technologies and tools can help in higher-end technological implementation towards Industry 4.0 [116], such as IoT in construction [105]. Construction employees’ digital literacy enables effective and efficient management of the relevant technologies and projects in developing and developed countries [114,115,117]. The importance is evident as the relevant software and concepts, such as BIM [118] and MATLAB, solve practical civil engineering problems [111]. These are and shall be taught to construction-related students [113]. In addition, being digitally literate makes construction degree graduates eligible for prospective construction jobs [22,26,93,112].

3.5.6. Digitisation and Virtualisation

Due to the digitalisation trends, an enormous amount of data is produced during the project lifecycle. To collect, store, manage, map, analyse, and visualise such massive data, concepts and tools such as big data, blockchain, cloud computing and collaboration, data analytics, GIS, and laser and lidar scanning are utilised [18,22,23,31,103,146,147,148,149], benefitting several construction processes [97]. The utilisation and need for such digital competencies are evident from the relevant literature, emphasising the demand for digital skills in the future [5,18,23] for civil engineering and allied disciplines, aiding in the transition to Construction 4.0 [97].

3.5.7. Modelling and Simulation

A few of the most essential and popular digital skills required today are related to the use of BIM design, modelling and simulation, mixed reality (MR), AR, and VR due to their vast benefits [18,23,73,103,130,150]. Simulation and modelling have been helpful decision-support tools [131] for several construction project aspects [151]. BIM has been adopted in the construction industry for a long time and almost in all phases and types of projects [152], such as for safety management [57] and energy efficiency analysis [153]. BIM has helped establish new working platforms [5], developing and expanding the professional, managerial, and digital capacities [86,101,104]. However, enhanced and widespread adoption is still required for future jobs [22,119,120] because of the current barriers BIM is facing in its due adoption [21], such as in design creation and coordination, as-built-modelling, clash detection, and other project management tasks [128].
In connection with this, researchers are investigating the current skill level of students and professionals regarding BIM and their future training needs [23,103,118,121,122,123,124]. Moreover, after analysing the barriers, researchers have suggested implementation recommendations and strategies at the organisational and government level [125,126,127]. In addition, AR and VR have also been beneficial in educating personnel and preventing quality and safety issues [154]. Furthermore, AR and VR technological skills have proven highly effective in automated progress monitoring and safe working environments [73]. In addition, the digital twin concept has recently been established in the construction industry [155], aiding monitoring and facility management [156]. Although Construction 4.0 utilises the above-identified tools and technologies, the stakeholders are not ready for such an implementation [157]. The lack of relevant digital skills hinders their due implementation [18,158]. Strategies such as digital and cultural transformation and bridging the skills gap must be implemented to transition towards technologies such as BIM and digital twins [129].

3.5.8. Planning and Estimation

Innovative changes in the construction industry, such as in quantity surveying and construction management areas, are compelling the relevant construction industry professionals to possess modern skills to utilise tools, technologies, and software [104,159]. Productivity planning, scheduling and cost estimation, and optimisation via programming, technology, and software, e.g., Dynamo, Navisworks, Primavera, MS Project, and Vico schedule planners, are already happening in the industry and lead to productivity improvement [22,84,99,133,159,160,161,162,163,164]. However, efficient planning and estimation skills are not sufficiently developed among the graduates and must be effectively taught [23,118]. Currently, these are not aligned with what is expected from industry practitioners [165] to be able to work with modern and intelligent technological tools and devices [117].
It can be seen from the developed taxonomy [89], as presented in Table 2, that the enlisted digital skills relevant to the construction industry’s needs are quite diverse. The research articles mainly focused on developing, using, or needing these individual skills, skipping an accumulative presentation of the needed skills. However, from the co-occurrence network of keywords (Figure 7) analysis, it is distinct that most of the foundational concepts and knowledge domains in the considered articles, such as [85,119,121,122,123], significantly lean towards BIM. These articles either focus on BIM skills and their development or BIM adoption and its barriers. Though this emphasises the importance of BIM skills and expertise, other digital skills are also gaining gradual significance in the academic and industry domains. Hence, as identified earlier in the literature, these skills must be effectively and collectively taught to future professionals.
The research for assessing the required digital skills is ongoing in all sectors, including construction and manufacturing, because technological change and upgrades are too fast. As discussed in the earlier sections, the digital skills of the construction industry need consolidation, and as a result, this taxonomy has been developed. Similarly, other sectors, such as the manufacturing industry, to which the construction industry is usually compared and contrasted, also have related developments. Though the manufacturing industry is more advanced than the construction industry, the taxonomy development of digital skills is progressive and evolving.
Several publications have presented the digital skills needed in the manufacturing sector. Per a report from Tulip [166], the essential digital skills required in the manufacturing industry include digital fluency, proficiency in writing and understanding code, competency in programming manufacturing-specific machines and devices, machining, fabrication, complex assembly, big data analytics, and robotics. Additional investigation indicates a growing significance of digital skills within the manufacturing industry. For example, Leitão et al. [167] underscore the significance of non-technical and technical digital skills across various manufacturing domains. Also, Azmat et al. [168] emphasise the necessity for workers to be equipped with digital skills in the age of industrial digitalisation. In addition, Akyazi et al. [169] created a skill database tailored for the manufacturing sector, encompassing anticipated future skill requisites for specific jobs.
Similarly, the researchers identified pivotal technical proficiencies and domain-specific knowledge requisite for data science and intelligent manufacturing roles. Likewise, Jurczuk and Florea [170] pinpointed deficiencies in digital skills in designing, implementing, and utilising solutions for automating and robotising business processes. They also developed a forward-looking framework for digital design competence to bridge these gaps. Florea [5], on the other hand, zeroes in on the imperative for educational institutions to align engineering education with the competencies essential to future factories. This analysis deduced potential competency requirements for Factory of the Future employees.
Furthermore, Salminen et al. [171] emphasised the imperative for industry and research providers to collaborate in bolstering technology management regarding skills and research. Moreover, Li et al. [172] assert that contemporary manufacturing professionals must undergo training in advanced, data-rich, computer-automated technologies. In the future, companies will require personnel possessing specialised skills in IoT-integrated additive manufacturing throughout the value stream. This encompasses proficiency in CAD, machine operation, raw material development, robotics, and supply chain management. These research works substantiate that digital skills are pivotal in the manufacturing industry’s transition towards Industry 4.0, underscoring the urgency for skill enhancement and educational initiatives. However, it is crucial to note that while these skills represent excellence within Industry 4.0, they do not singularly constitute the comprehensive prerequisites of the manufacturing process [173].
Although there might be certain similarities between manufacturing and construction, significant distinctions exist concerning product complexity, safety risks, organisational structure, and the distinct nature of construction projects. Nonetheless, it is observed that the developed taxonomy of the construction industry in this research article and the skills presented by the manufacturing industry have some similarities and differences, mainly because the former is less digitalised. Because the manufacturing industry is already more digitalised, the relevant required digital skills are inclined towards robotics, code development, big data, automation technologies, CAD, additive manufacturing, and many more.

4. Conclusions

The construction industry has been relatively slow in adopting digital technologies compared to others, and one of the reasons for this is the lack of relevant skills and proper understanding. Therefore, the current study aimed to investigate the most currently used and needed digital skills in the construction industry. Initially, it followed the scientometric analysis methodology to evaluate the trends, outlets, co-authorship, and keyword co-occurrence networks in the published literature. The publication trend results implied that publishing in conference proceedings remained common in this area, but journal publications were also evident later. Scholars typically evaluate the reception of their new concepts at conferences, where they obtain early feedback before publishing in academic journals. Furthermore, conference publications can be advantageous for pioneering topics with minimal supporting literature, as they offer a valuable platform for discussing and disseminating original research findings.
Observing the diverse range of publication outlets dictates that various authors target multiple outlets to publish their research findings. The publication trends and outlet results provide insights to future researchers on where to publish their scholarship, possibly in journal outlets, to reach a broad audience and enhance the credibility and quality of their research. Researchers must select the most appropriate publication outlet for their research to ensure it reaches its intended audience and has the most significant impact.
Concerning the scientific collaboration network diagram, it is revealed that there is a lack of collaboration and networking in the studied research area. As a result, many have disregarded knowledge creation and dissemination through collaborative research. Therefore, there is a need for greater attention and effort to forming collaborative networks for future works. Furthermore, developing a collaborative network amongst different departments, universities, and countries would help identify the digital skills needed and utilisation around the globe. It could eventually lead to global need analysis and aid in systematically dealing with the skills shortage. Furthermore, the keyword co-occurrence analyses reveal that the concept of BIM is under the limelight despite the industry’s need for several other skills. It is, therefore, recommended that scholars investigate different skills needs and development.
In the second phase of the research, the article mainly contributed to listing and categorising the most used and needed digital skills through a rigorous SLR process. The identified digital skills were organised into a taxonomy. These digital skills were categorised into automation and robotics, coding and programming, design, drafting and engineering, digital data acquisition and integration, digital literacy, digitisation and virtualisation, modelling and simulation, and planning and estimation. In contrast to past literature, which focused on specific skills investigation such as for BIM, IoT, ICT, and IT, or any specific job roles at the managerial and professional level, the shortlisting of the digital skills in this work was not specified to any job position. Instead, it included diverse digital skills ranging across professions. Therefore, the taxonomy could benefit many stakeholders, whether on-site or office-based staff.

5. Implications

The developed taxonomy contributes to practise as it would aid company-wide skills review of the existing staff base. This benchmarking will allow the companies to reevaluate and improve depending on their needs and strategic goals. The developed taxonomy can also be further assessed through a specific contextual point of view. As each country’s construction industry technology adoption and digitalisation maturity level will be different, possibly there would be a difference in their digital skills need and utilisation. This warrants that the industry be assessed at national and international levels to identify the respective digital skills in demand. Such an analysis could help formulate future policies for the contextual construction industry. Also, the relevant institutes offering their services to train the concerned stakeholders must ensure that the contextual needs of digital skills are considered.
Additionally, such a taxonomy of digital skills becomes a foundation and the first point of contact for academia. It will help academia upgrade its existing infrastructure capabilities and technological and human capacity. Human resources can be re-skilled and up-skilled if they are not capable enough. Moreover, dedicating resources to education and training initiatives can be crucial to propel the industry into the digital age. By tackling these impediments to the digitalisation of the construction industry, the industry can significantly improve productivity, cut costs, and promote sustainability.
On the other hand, regarding the publishing domain and academic community, high-quality construction digitalisation journals can better serve the bodies of knowledge and practices by expanding their scope from using technology to improve the state of practice to teaching the relevant digital skills to improve human resources, who can then use the technology. Moreover, the existing construction education research-oriented journal outlets could also be targeted for relevant publications.

6. Limitations and Future Directions

Though the study has undergone a rigorous article collection and analysis process utilising SLR methodology, additional extensive content analysis methodology can also be employed for future works. It will help code and categorise the skills at deeper levels. In addition, more analyses can be conducted through VOSviewer, such as co-citation network analysis and bibliographic cluster analysis, to understand the bibliometric features further. Moreover, the keyword co-occurrence analyses reveal that the concept of BIM is in the limelight despite the industry’s need for several other skills. The literature review inherently was mainly BIM-focused because of the popularity of BIM. Undoubtedly, several other skills are also linked to BIM’s existence, usage, and dependency. However, it is recommended that scholars investigate different skills needs and development.
Based on the presented importance of the developed digital skills taxonomy, one of the critical future works that can be undertaken shall be based on each country’s construction industry. It could help to ascertain the contextual needs of digital skills, and later, the global perspective could be established. This work is inherently evolving because of the underlying foundation of digitalisation and relevant digital technologies in demand and use. The ever-changing nature of technology adoption and the swiftly evolving landscape of Industry 4.0 could soon lead to some findings becoming obsolete. Dozens of relevant research works are being regularly published. Future works within this area shall be conducted at regular intervals to visualise the trendline of how the technologies are implemented and improving in AEC and what related digital skills are desired to remain updated and in competition.

Author Contributions

Conceptualization, F.H.S., M.J.T. and A.A.; methodology, F.H.S., M.J.T. and A.A.; software, F.H.S.; validation, F.H.S. and M.J.T.; analysis, F.H.S.; resources, F.H.S.; data curation, F.H.S. and M.J.T.; writing—original draft preparation, F.H.S.; writing—review and editing, M.J.T. and A.A.; visualization, F.H.S.; supervision, M.J.T. and A.A.; project administration, M.J.T. and A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created or analysed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Keywords Combinations and Relevant Search Results.
Table A1. Keywords Combinations and Relevant Search Results.
S. NoKeywords CombinationsScopus Search ResultsWoS Search Results
1C DS9446
2C DS D136
3C DS D E20
4C DS D E S10
5C DS D S51
6C DS D SLR10
7C DS D SLR E10
8C DS D SLR E S10
9C DS D SLR S10
10C DS D T21
11C DS D T S10
12C DS E107
13C DS E S52
14C DS S2112
15C DS SLR186
16C DS SLR E10
17C DS SLR E S10
18C DS SLR S21
19C DS SLR T53
20C DS SLR T S11
21C DS T2317
22C DS T E44
23C DS T E S31
24C DS T S74
25DS D E32
26DS D E S21
27DS D S73
28DS D SLR E21
29DS D SLR E S20
30DS D SLR S21
31DS D T S10
32DS E1210
33DS E S63
34DS S2715
35DS SLR E21
36DS SLR E S20
37DS SLR S33
38DS SLR T S11
39DS T E55
40DS T E S31
41DS T S54

References

  1. Puolitaival, T.; Davies, K.; Kähkönen, K. Digital technologies and related competences in construction management in the era of fast-paced digitalisation. In Proceedings of the CIB World Building Congress 2019, Hong Kong, China, 17–21 June 2019. [Google Scholar]
  2. Osunsanmi, T.O.; Aigbavboa, C.; Oke, A. Construction 4.0: The Future of South Africa Construction Industry. World Acad. Sci. Eng. Technol. Int. J. Civ. Environ. Eng. 2018, 12, 206–212. [Google Scholar]
  3. Leviäkangas, P.; Paik, S.M.; Moon, S. Keeping up with the pace of digitization: The case of the Australian construction industry. Technol. Soc. 2017, 50, 33–43. [Google Scholar] [CrossRef]
  4. Agarwal, R.; Chandrasekaran, S.; Sridhar, M. Imagining Construction’s Digital Future. McKinsey. 24 June 2016. Available online: https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/imagining-constructions-digital-future (accessed on 8 June 2020).
  5. García de Soto, B.; Agustí-Juan, I.; Joss, S.; Hunhevicz, J. Implications of Construction 4.0 to the workforce and organizational structures. Int. J. Constr. Manag. 2022, 22, 205–217. [Google Scholar] [CrossRef]
  6. Soltani, S.; Maxwell, D.; Rashidi, A. The State of Industry 4.0 in the Australian Construction Industry: An Examination of Industry and Academic Point of View. Buildings 2023, 13, 2324. [Google Scholar] [CrossRef]
  7. Khahro, S.H.; Hassan, S.; Zainun NY, B.; Javed, Y. Digital transformation and e-commerce in construction industry: A prospective assessment. Acad. Strateg. Manag. J. 2021, 20, 1–8. [Google Scholar]
  8. Lukac, D. The fourth ICT-based industrial revolution ‘Industry 4.0′—HMI and the case of CAE/CAD innovation with EPLAN P8. In Proceedings of the 2015 23rd Telecommunications Forum Telfor (TELFOR), Belgrade, Serbia, 24–25 November 2015; pp. 835–838. [Google Scholar] [CrossRef]
  9. Chu, M.; Matthews, J.; Love, P.E. Integrating mobile Building Information Modelling and Augmented Reality systems: An experimental study. Autom. Constr. 2018, 85, 305–316. [Google Scholar] [CrossRef]
  10. Karji, A.; Woldesenbet, A.; Rokooei, S. Integration of Augmented Reality, Building Information Modeling, and Image Processing in Construction Management: A Content Analysis. In Proceedings of the AEI, Oklahoma City, OK, USA, 11–13 April 2017; pp. 983–992. [Google Scholar] [CrossRef]
  11. Van De Wetering, J.; Dixon, T.; Sexton, M. Smart Cities, Big Data and the Built Environment: What’s Required. In Proceedings of the 23rd Annual European Real Estate Society Conference, Regensburg, Germany, 8–11 June 2016. [Google Scholar] [CrossRef]
  12. Jesse, N. Internet of Things and Big Data: The disruption of the value chain and the rise of new software ecosystems. AI Soc. 2018, 33, 229–239. [Google Scholar] [CrossRef]
  13. Nassereddine, H.; Veeramani, A.; Veeramani, D. Exploring the Current and Future States of Augmented Reality in the Construction Industry; Springer International Publishing: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
  14. Tayeh, R.; Bademosi, F.; Issa, R.R. Information Systems Curriculum for Construction Management Education. In Proceedings of the Construction Research Congress 2020, Reston, VA, USA, 8–10 March 2020; pp. 800–809. [Google Scholar]
  15. Koretsky, M.D.; Magana, A.J. Using technology to enhance learning and engagement in engineering. Adv. Eng. Educ. 2019, 7, 1–53. [Google Scholar]
  16. Khadim, N.; Agliata, R.; Thaheem, M.J.; Mollo, L. Whole building circularity indicator: A circular economy assessment framework for promoting circularity and sustainability in buildings and construction. Build. Environ. 2023, 241, 110498. [Google Scholar] [CrossRef]
  17. Agrawal, R.; Yadav, V.S.; Majumdar, A.; Kumar, A.; Luthra, S.; Garza-Reyes, J.A. Opportunities for disruptive digital technologies to ensure circularity in supply Chain: A critical review of drivers, barriers and challenges. Comput. Ind. Eng. 2023, 178, 109140. [Google Scholar] [CrossRef]
  18. Suprun, E.; Perisic, N.; Stewart, R.; Mostafa, S. Preparing the Next Generation of Civil Engineering Graduates: Identifying and Combating the Digital Skills Gap. In Proceedings of the 30th Annual Conference for the Australasian Association for Engineering Education (AAEE 2019): Edu-cators Becoming Agents of Change: Innovate, Integrate, Motivate, Brisbane, Australia, 8–11 December 2019. [Google Scholar]
  19. Perera, S.; Ingirige, B.; Ruikar, K.; Obonyo, E. Advances in Construction ICT and e-Business; Taylor & Francis Ltd.: London, UK, 2017. [Google Scholar] [CrossRef]
  20. Kamaruddin, S.S.; Mohammad, M.F.; Mahbub, R. Barriers and Impact of Mechanisation and Automation in Construction to Achieve Better Quality Products. Procedia Soc. Behav. Sci. 2016, 222, 111–120. [Google Scholar] [CrossRef]
  21. Sriyolja, Z.; Harwin, N.; Yahya, K. Barriers to Implement Building Information Modeling (BIM) in Construction Industry: A Critical Review. IOP Conf. Ser. Earth Environ. Sci. 2021, 738, 012021. [Google Scholar] [CrossRef]
  22. Fitriani, H.; Ajayi, S. Preparing Indonesian civil engineering graduates for the world of work. Ind. High Educ. 2022, 36, 471–487. [Google Scholar] [CrossRef]
  23. Balogun, T.B.; Awonuga, O.O.; Abowen-Dake, R. Investigating digital technological competencies amongst black Asian minority ethnic construction students in the UK. J. Eng. Des. Technol. 2021. [Google Scholar] [CrossRef]
  24. Van Deursen, A.; van Dijk, J. Loss of labor time due to malfunctioning ICTs and ICT skill insufficiencies. Int. J. Manpow. 2014, 35, 703–719. [Google Scholar] [CrossRef]
  25. Francis, V.; Paton-Cole, V. Innovation and Immersive Vocational Education Training for Construction Site Supervisors. In Collaboration and Integration in Construction, Engineering, Management and Technology; Springer: Cham, Switzerland, 2021; pp. 179–183. [Google Scholar] [CrossRef]
  26. Department of Education and Training. Victorian Employer Skills and Training Survey 2017; VIC Government: Melbourne, VIC, Australia, 2017.
  27. Chowdhury, T.; Adafin, J.; Wilkinson, S. Review of digital technologies to improve productivity of New Zealand construction industry. J. Inf. Technol. Constr. 2019, 24, 569–587. [Google Scholar] [CrossRef]
  28. Becker, S.A.; Pasquini, L.A.; Zentner, A. 2017 Digital Literacy Impact Study: An NMC Horizon Project Strategic Brief; The New Media Consortium: Austin, TX, USA, 2017. [Google Scholar]
  29. Djumalieva, J.; Sleeman, C. Which Digital Skills Do You Really Need? London Nesta. 2018. Available online: https//www.nesta.org.uk/report/which-digital-skills-do-you-really-need (accessed on 1 March 2023).
  30. Bergson-Shilcock, A.; Taylor, R.; Hodge, N. Closing the digital skill divide. Natl. Skills Coalit. 2023, 390, 8619. [Google Scholar]
  31. Oesterreich, T.D.; Teuteberg, F. Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Comput. Ind. 2016, 83, 121–139. [Google Scholar] [CrossRef]
  32. Perrier, N.; Bled, A.; Bourgault, M.; Cousin, N.; Danjou, C.; Pellerin, R.; Roland, T. Construction 4.0: A Survey of Research Trends. J. Inf. Technol. Constr. 2020, 25, 416–437. [Google Scholar] [CrossRef]
  33. Wagire, A.A.; Rathore AP, S.; Jain, R. Analysis and synthesis of Industry 4.0 research landscape. J. Manuf. Technol. Manag. 2019, 31, 31–51. [Google Scholar] [CrossRef]
  34. Lichtenthaler, U. Building Blocks of Successful Digital Transformation: Complementing Technology and Market Issues. Int. J. Innov. Technol. Manag. 2020, 17, 2050004. [Google Scholar] [CrossRef]
  35. Forcada Matheu, N. Life Cycle Document Management System for Construction. Ph.D. Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2005. [Google Scholar]
  36. Zhang, S.; Lee, J.K.; Venugopal, M.; Teizer, J.; Eastman, C. Integrating BIM and Safety: An Automated Rule-Based Checking System for Safety Planning and Simulation. In Proceedings of the CIB W099 Conference, Washington, DC, USA, 24–26 August 2011; pp. 1–13. [Google Scholar]
  37. Saccardo, D.; Langston, C. The Impact of Emerging Technology on the Value of Construction Projects. 2020. Available online: https://bond.edu.au/files/5115/Saccardo%20report.pdf (accessed on 18 May 2023).
  38. Stewart, R.A.; Mohamed, S.; Daet, R. Strategic implementation of IT/IS projects in construction: A case study. Autom. Constr. 2002, 11, 681–694. [Google Scholar] [CrossRef]
  39. Dernis, H.; Dosso, M.; Hervas, F.; Millot, V.; Squicciarini, M.; Vezzani, A. World Corporate Top R&D Investors: Innovation and IP bundles; Publications Office of the European Union: Luxembourg, 2015. [Google Scholar] [CrossRef]
  40. Pena-Mora, F.; Vadhavkar, S.; Dirisala, S.K. Component-based software development for integrated construction management software applications. Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM 2001, 15, 173–187. [Google Scholar] [CrossRef]
  41. Abudayyeh, O.; Cai, H.; Fenves, S.J.; Law, K.; O’Neill, R.; Rasdorf, W. Assessment of the computing component of civil engineering education. J. Comput. Civ. Eng. 2004, 18, 187–195. [Google Scholar] [CrossRef]
  42. Gerber, D.J.; Khashe, S.; Smith, I.F. Surveying the Evolution of Computing in Architecture, Engineering, and Construction Education. J. Comput. Civ. Eng. 2015, 29, 04014060. [Google Scholar] [CrossRef]
  43. Lo, J.T.Y.; Kam, C. Innovation Performance Indicators for Architecture, Engineering and Construction Organization. Sustainability 2021, 13, 9038. [Google Scholar] [CrossRef]
  44. Lo, J.T.; Kam, C. Innovation of Organizations in the Construction Industry: Progress and Performance Attributes. J. Manag. Eng. 2022, 38, 04022064. [Google Scholar] [CrossRef]
  45. Zainon, N.; Rahim, F.A.; Salleh, H. The Information Technology Application Change Trend: Its Implications for the Construction Industry. J. Surv. Constr. Prop. 2011, 2, 1–15. [Google Scholar] [CrossRef]
  46. Forcael, E.; Ferrari, I.; Opazo-Vega, A.; Pulido-Arcas, J.A. Construction 4.0: A Literature Review. Sustainaiblity 2020, 12, 9755. [Google Scholar] [CrossRef]
  47. Sawhney, A.; Riley, M.; Irizarry, J.; Riley, M. Construction 4.0: An Innovation Platform for the Built Environment; Routledge: Abingdon, UK, 2020. [Google Scholar]
  48. Alaloul, W.S.; Liew, M.S.; Zawawi NA, W.A.; Mohammed, B.S. Industry Revolution IR 4.0: Future Opportunities and Challenges in Construction Industry. MATEC Web Conf. 2018, 203, 02010. [Google Scholar] [CrossRef]
  49. WEF. Shaping the Future of Construction A Breakthrough in Mindset and Technology. 2016. Available online: https://www3.weforum.org/docs/WEF_Shaping_the_Future_of_Construction_report_020516.pdf (accessed on 8 May 2023).
  50. ESCO. Digitalisation in the Construction Sector; European Construction Sector Observatory: Brussels, Belgium, 2021; pp. 80–94. [Google Scholar]
  51. Perera, S.; Jin, X.; Samaratunga, M.; Thalagala Achchi Maddumage, K.G. Construct NSW: Digitalisation of Construction Construct NSW Industry Report on Digitalisation of Design and Construction of Class 2 Buildings in New South Wales; Western Sydney University: Sydney, Australia, 2021. [Google Scholar] [CrossRef]
  52. Ahmed, D.; Nayeemuddin, M.; Ayadat, T.; Asiz, A. Computing Competency for Civil Engineering Graduates: Recent Updates and Developments in Saudi Arabia and the US. Int. J. High Educ. 2021, 10, 57. [Google Scholar] [CrossRef]
  53. Talaat, A.; Kohail, M.; Ahmed, S.M. Programming in The Context of Civil Engineering Education. 2022. Available online: https://assets.researchsquare.com/files/rs-1802246/v1/7f344a5a-15e0-4d04-a827-b115cb4ac713.pdf?c=1660232211 (accessed on 19 May 2023).
  54. Akyazi, T.; Alvarez, I.; Alberdi, E.; Oyarbide-Zubillaga, A.; Goti, A.; Bayon, F. Skills needs of the civil engineering sector in the european union countries: Current situation and future trends. Appl. Sci. 2020, 10, 7226. [Google Scholar] [CrossRef]
  55. De Cicco, R. Digital Skills Require a Culture of Continuous Learning. Construction Manager. 2018. Available online: https://www.constructionmanagermagazine.com/digital-skills-require-culture-continuous-learning/ (accessed on 7 October 2020).
  56. Mingers, J.; Leydesdorff, L. A review of theory and practice in scientometrics. Eur. J. Oper. Res. 2015, 246, 1–19. [Google Scholar] [CrossRef]
  57. Akram, R.; Thaheem, M.J.; Nasir, A.R.; Ali, T.H.; Khan, S. Exploring the role of building information modeling in construction safety through science mapping. Saf. Sci. 2019, 120, 456–470. [Google Scholar] [CrossRef]
  58. Hasan, A.; Ghosh, A.; Mahmood, M.N.; Thaheem, M.J. Scientometric review of the twenty-first century research on women in construction. J. Manag. Eng. 2021, 37, 4021004. [Google Scholar] [CrossRef]
  59. Norouzi, M.; Chàfer, M.; Cabeza, L.F.; Jiménez, L.; Boer, D. Circular economy in the building and construction sector: A scientific evolution analysis. J. Build. Eng. 2021, 44, 102704. [Google Scholar] [CrossRef]
  60. Van Eck, N.J.; Waltman, L. VOSviewer Manual. 2023. Available online: https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.19.pdf (accessed on 9 March 2023).
  61. Waltman, L.; van Eck, N.J.; Noyons, E.C.M. A unified approach to mapping and clustering of bibliometric networks. J. Informetr. 2010, 4, 629–635. [Google Scholar] [CrossRef]
  62. Lima, L.; Trindade, E.; Alencar, L.; Alencar, M.; Silva, L. Sustainability in the construction industry: A systematic review of the literature. J. Clean. Prod. 2020, 289, 125730. [Google Scholar] [CrossRef]
  63. Siddaway, A. What is a Systematic Literature Review and How Do I Do One? 2014. Available online: https://create.twu.ca/drheatherstrong/files/2018/02/WHAT-IS-A-SYSTEMATIC-LITERATURE-REVIEW-AND-HOW-DO-I-DO-ONE.pdf (accessed on 1 October 2022).
  64. Von Danwitz, S. Managing inter-firm projects: A systematic review and directions for future research. Int. J. Proj. Manag. 2018, 36, 525–541. [Google Scholar] [CrossRef]
  65. Tawfik, G.M.; Dila, K.A.S.; Mohamed, M.Y.F.; Tam, D.N.H.; Kien, N.D.; Ahmed, A.M.; Huy, N.T. A step by step guide for conducting a systematic review and meta-analysis with simulation data. Trop. Med. Health 2019, 47, 46. [Google Scholar] [CrossRef]
  66. Mahasneh, J.K.; Thabet, W. Rethinking Construction Curriculum: Towards a Standard Soft Skills Taxonomy. In Proceedings of the 52nd ASC Annual International Conference, Provo, UT, USA, 13–16 April 2016. [Google Scholar]
  67. Nijhuis, S.; Vrijhoef, R.; Kessels, J. Towards a Taxonomy for Project Management Competences. Procedia Soc. Behav. Sci. 2015, 194, 181–191. [Google Scholar] [CrossRef]
  68. Ghaleb, H.; Alhajlah, H.H.; Bin Abdullah, A.A.; Kassem, M.A.; Al-Sharafi, M.A. A Scientometric Analysis and Systematic Literature Review for Construction Project Complexity. Buildings 2022, 12, 482. [Google Scholar] [CrossRef]
  69. Kamardeen, I.; Samaratunga, M. Digiexplanation driven assignments for personalising learning in construction education. Constr. Econ. Build. 2020, 20, 103–123. [Google Scholar] [CrossRef]
  70. Safin, R.; Korchagin, E.; Vildanov, I.; Abitov, R. On professional and pedagogical competence development of technical university teaching staff. IOP Conf. Ser. Mater. Sci. Eng. 2020, 890, 012167. [Google Scholar] [CrossRef]
  71. Putri, E.E.; Ratu, E.K. Priority setting for competency development training topics for road construction site managers to reduce the risk of construction failure. MATEC Web Conf. 2018, 229, 1003. [Google Scholar]
  72. Lim, B.T.; Ling, F.Y. Contractors’ human resource development practices and their effects on employee soft skills. Archit. Sci. Rev. 2011, 54, 232–245. [Google Scholar] [CrossRef]
  73. Elzomor, M.; Pradhananga, P. Scaling Construction Autonomous Technologies and Robotics within the Construction Industry. In Proceedings of the 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, Virtual, 26 July 2021. [Google Scholar]
  74. Career, Y. What Are Digital Skills & Why Are They Important? UNLV. Australian Government; 2021. Available online: https://www.yourcareer.gov.au/articles/what-are-digital-skills-and-why-are-they-in-demand (accessed on 28 August 2022).
  75. Gekara, V.; Molla, A.; Snell, D.; Karanasios, S.; Thomas, A. Developing Appropriate Workforce Skills for Australia’s Emerging Digital Economy; National Centre for Vocational Education Research: Adelaide, SA, Australia, 2017. [Google Scholar]
  76. Beck, S.; Mahdad, M.; Beukel, K.; Poetz, M. The Value of Scientific Knowledge Dissemination for Scientists—A Value Capture Perspective. Publications 2019, 7, 54. [Google Scholar] [CrossRef]
  77. Macauley, P.; Green, R. Supervising publishing from the doctorate. In Supervising Doctorates Downunder: Keys to Effective Supervision in Australia and New Zealand; Denholm, C.J., Evans, T., Eds.; Acer Press: Melbourne, Asutralia, 2007; pp. 192–199. [Google Scholar]
  78. Automation in Construction. Journal ScienceDirect.com by Elsevier. 2023. Available online: https://0-www-sciencedirect-com.brum.beds.ac.uk/journal/automation-in-construction?_gl=1*1scxj66*_ga*MTcwOTY5MTQxOC4xNjY2ODY0NTY3*_ga_4R527DM8F7*MTY4MTM1Nzg5MS4zLjAuMTY4MTM1Nzg5MS4wLjAuMA.. (accessed on 14 April 2023).
  79. ASCE Library. Aims & Scope and Editorial Board: Journal of Computing in Civil Engineering. 2023. Available online: https://ascelibrary.org/page/jccee5/editorialboard (accessed on 14 April 2023).
  80. ASCE Library. Aims & Scope and Editorial Board: Journal of Civil Engineering Education. Available online: https://ascelibrary.org/page/jceecd/editorialboard (accessed on 17 May 2023).
  81. International Journal of Construction Education and Research Aims & Scope. Available online: https://0-www-tandfonline-com.brum.beds.ac.uk/action/journalInformation?show=aimsScope&journalCode=uice20 (accessed on 17 May 2023).
  82. Ding, Y. Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. J. Informetr. 2011, 5, 187–203. [Google Scholar] [CrossRef]
  83. Glänzel, W.; Schubert, A. Analysing Scientific Networks Through Co-Authorship. In Handbook of Quantitative Science and Technology Research; Moed, H.F., Glänzel, W., Schmoch, U., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2004; pp. 257–276. [Google Scholar] [CrossRef]
  84. Mandicak, T.; Mesaros, P.; Spisakova, M.; Behun, M.; Kanalikova, A. The Knowledge Competencies and Digital Competencies of Project Managers in Life Cycle Cost Management. In Proceedings of the 2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA), Kosice, Slovenia, 12–13 November 2020; pp. 438–443. [Google Scholar] [CrossRef]
  85. Mandičák, T.; Mésároš, P.; Behún, M.; Behúnová, A. Development of Digital and Managerial Competencies and BIM Technology Skills in Construction Project Management. EAI/Springer Innov. Commun. Comput. 2020, 159–175. [Google Scholar] [CrossRef]
  86. Mesároš, P.; Mandičák, T.; Mesárošová, A.; Behún, M. Developing managerial and digital competencies trough BIM technologies in construction industry. In Proceedings of the 2016 International Conference on Emerging eLearning Technologies and Applications (ICETA), Stary Smokovec, Slovak Republic, 24–25 November 2016; pp. 217–222. [Google Scholar] [CrossRef]
  87. Su, H.N.; Lee, P.C. Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics 2010, 85, 65–79. [Google Scholar] [CrossRef]
  88. Van Eck, N.J.; Waltman, L. Visualizing Bibliometric Networks BT—Measuring Scholarly Impact: Methods and Practice; Ding, Y., Rousseau, R., Wolfram, D., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 285–320. [Google Scholar] [CrossRef]
  89. Siddiqui, F.H.; Abdekhodaee, A.; Thaheem, M.J. Taxonomy of Digital Skills Needed in the Construction Industry: A Literature Review. In Proceedings of the AUBEA 2022: The 45th Australasian Universities Building Education Association Conference, Sydney, New South Wales, Australia, 23–25 November 2022; pp. 819–828. [Google Scholar]
  90. Li, Y.; Liu, C. Applications of multirotor drone technologies in construction management. Int. J. Constr. Manag. 2019, 19, 401–412. [Google Scholar] [CrossRef]
  91. Hossain, M.A.; Zhumabekova, A.; Paul, S.C.; Kim, J.R. A Review of 3D Printing in Construction and its Impact on the Labor Market. Sustainability 2020, 12, 8492. [Google Scholar] [CrossRef]
  92. Siddiqui, F.; Garzon, L.; Cole, G.; Polkinghorn, T.; Gad, E.; Moon, S. Development of RFID-aided system for reducing inspection time in offsite construction project. In Proceedings of the SBE 19 Seoul, International Conference on Sustainable Built Environment (SBE), Smart Building and City for Durability & Sustainability, Seoul, Republic of Korea, 12–13 December 2019; pp. 211–214. [Google Scholar]
  93. Ryan, A.; Bouchard, C.; Fitzpatrick, C.; Knodler, M., Jr.; Ahmadjian, C. Analytical Comparison of Core Competencies across Civil Engineering Positions within New England Department of Transportation Agencies. Transp. Res. Rec. 2019, 2673, 427–437. [Google Scholar] [CrossRef]
  94. Zulu, S.L.; Saad, A.M.; Omotayo, T. The Mediators of the Relationship between Digitalisation and Construction Productivity: A Systematic Literature Review. Buildings 2023, 13, 839. [Google Scholar] [CrossRef]
  95. Gerbert, P.; Castagnino, S.; Rothballer, C.; Renz, A.; Filitz, R. Digital in Engineering and Construction: The Transformative Power of Building Information Modeling. 2016. Available online: https://www.bcg.com/publications/2016/engineered-products-infrastructure-digital-transformative-power-building-information-modeling (accessed on 13 May 2023).
  96. Tjebane, M.M.; Musonda, I.; Okoro, C. Organisational Factors of Artificial Intelligence Adoption in the South African Construction Industry. Front. Built Environ. 2022, 8, 823998. [Google Scholar] [CrossRef]
  97. Afful, A.E.; Acquah GK, K.; Baah, B. Systemic Capacity Building of Built Environment Professionals for Construction 4.0: A Review of Concepts. In Emerging Debates in the Construction Industry; Routledge: London, UK, 2023; pp. 193–206. [Google Scholar] [CrossRef]
  98. Jallow, H.; Renukappa, S.; Suresh, S.; Rahimian, F. Artificial Intelligence and the UK Construction Industry—Empirical Study. Eng. Manag. J. 2022, 1–14. [Google Scholar] [CrossRef]
  99. Mondragon Solis, F.A.; Howe, J.; O’Brien, W.J. Integration of Information Technologies into Field Managers’ Activities: A Cognitive Perspective. J. Manag. Eng. 2015, 31, A4014001. [Google Scholar] [CrossRef]
  100. Shayakhmetov, U.; Larkina, A.; Khalikov, R.; Sinitsin, D.; Nadoseko, I. Methodological tools for university transfer of high-demand nanotechnologies to the regional building industry. Nanotechnologies Constr. Sci. Internet-J. 2021, 13, 12–17. [Google Scholar] [CrossRef]
  101. Vaganova, O.I.; Smirnova, Z.V.; Kozlova, O.P.; Vishnevetskaya, N.A.; Prostov, A.V. Formation of professional competencies of future builders in technical school. IOP Conf. Ser. Mater. Sci. Eng. 2020, 828, 0120229. [Google Scholar] [CrossRef]
  102. Jayawickrama, T.; Abdelaal, S.; Abadia, R. Using online learning environments to address digital literacy competencies of construction management graduates. ICCE 2020 28th Int. Conf. Comput. Educ. Proc. 2020, 1, 670–679. [Google Scholar]
  103. Agyemang, D.Y.; Fong, P. Towards desirable skill-set acquisition by construction management students: A systematic review. In Proceedings of the 16th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning (ICICKM 2019), Sydney, Australia, 5–6 December 2019; pp. 397–406. [Google Scholar]
  104. Pariafsai, F.; Behzadan, A.H. Core Competencies for Construction Project Management: Literature Review and Content Analysis. J. Civ. Eng. Educ. 2021, 147, 04021010. [Google Scholar] [CrossRef]
  105. Madanayake, U.; Seidu, R.; Young, B. Investigating the Skills and Knowledge Requirements for IOT implementation in Construction. In Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, Detroit, MI, USA, 10–14 August 2020. [Google Scholar]
  106. Wright, E.R.; Hanus, J.P. Integration Of Information Technology Software In A Civil Engineering Program. In Proceedings of the 2014 ASEE Annual Conference & Exposition Proceedings, Indianapolis, IN, USA, 15–18 June 2014; pp. 1–11. [Google Scholar] [CrossRef]
  107. Ozumba, A.; Shakantu, W. Balancing site information and communication technology systems with available ICT skills. In Proceedings of the RICS Construction and Building Research Conference, Cape Town, South Africa, 10–11 September 2009; pp. 128–137. [Google Scholar]
  108. Nguyen, T.; O’Brien, W.; Schmidt, K. Construction student technology skill assessment: A survey instrument. In Proceedings of the Construction Research Congress 2009: Building a Sustainable Future, Seattle, WA, USA, 5–7 April 2009; pp. 497–505. [Google Scholar]
  109. Nguyen, T.; Schmidt, K.; O’Brien, W. Technology skill assessment of construction students and professional workers. In Proceedings of the 2008 Annual Conference & Exposition, Pittsburgh, PA, USA, 25 June 2008. [Google Scholar] [CrossRef]
  110. Villegas-Quezada, C.; de los Santos, R.; Villegas, C.R. Assessment of Complex Abilities in Engineering Students using Structural Equation Models and Multidimensional Item Response Theory: Preliminary Results. World Congr. Eng. 2007, 1, 558–562. [Google Scholar]
  111. Celaya, E.A.; Ribero, M.E.; Gomez, M.G.; Matute, J.M. Graphical resolution of engineering problems using mathematical software. In Proceedings of the 9th Annual International Conference of Education, Research and Innovation, Seville, Spain, 14–16 November 2016; Volume 1, pp. 6630–6638. [Google Scholar] [CrossRef]
  112. Windapo, A.O. The construction industry transformation and the digital divide: Bridging the gap. S. Afr. J. Sci. 2021, 117, 2019–2022. [Google Scholar] [CrossRef] [PubMed]
  113. Maghiar, M.; Song, X.; Brown, C. Employers’ Perceptions of Technology Competency and Graduates’ Readiness: A Multi-Disciplinary, Qualitative Analysis in the Southeastern United States. EDULEARN19 Proc. 2019, 1, 4713–4720. [Google Scholar] [CrossRef]
  114. Zulu, S.L.; Saad, A.M.; Gledson, B. Individual Characteristics as Enablers of Construction Employees’ Digital Literacy: An Exploration of Leaders’ Opinions. Sustainability 2023, 15, 1531. [Google Scholar] [CrossRef]
  115. Liu, H.; Zhang, H.; Zhang, R.; Jiang, H.; Ju, Q. Competence Model of Construction Project Manager in the Digital Era—The Case from China. Buildings 2022, 12, 1385. [Google Scholar] [CrossRef]
  116. Ebekozien, A.; Aigbavboa, C.O.; Adekunle, S.A.; Aliu, J.; Thwala, W.D. Training needs of built environment professionals: The role of fourth industrial revolution. Eng. Constr. Archit. Manag. 2023. [Google Scholar] [CrossRef]
  117. Ngo, J.; Hwang, B.G. Critical Project Management Knowledge and Skills for Managing Projects with Smart Technologies. J. Manag. Eng. 2022, 38, 05022013. [Google Scholar] [CrossRef]
  118. Vivas Urías, M.D.; Solar Serrano, P.D.; Peña González AD, L.; Andrés Ortega, S.; Liébana Carrasco, Ó. Inclusion of Building Information Modeling in the Development of Digital Competencies for the Building Engineering Degree. In Proceedings of the ICERI 2015 Conference Proceedings 8th International Conference of Education, Research and Innovation, Seville, Spain, 16–18 November 2015; pp. 3412–3418. [Google Scholar] [CrossRef]
  119. Karampour, B.; Mohamed, S.; Karampour, H.; Lupica Spagnolo, S. Formulating a Strategic Plan for BIM Diffusion within the AEC Italian Industry: The Application of Diffusion of Innovation Theory. J. Constr. Dev. Coutries 2021, 26, 161–184. [Google Scholar] [CrossRef]
  120. Hore, A.V. Proposal for a construction industry digital competency centre for Ireland. In Proceedings of the COBRA—The RICS Construction, Building and Real Estate Research Conference, Dublin, Ireland, 4–5 September 2008. [Google Scholar]
  121. Azhar, N.; Fadzil, S.F.S. Malaysian polytechnic architecture students’ readiness toward BIM adoption: A pilot study. AIP Conf. Proc. 2021, 2428, 070002. [Google Scholar] [CrossRef]
  122. Anderson, A.; Dossick, C.S.; Osburn, L. Curriculum to Prepare AEC Students for BIM-Enabled Globally Distributed Projects. Int. J. Constr. Educ. Res. 2020, 16, 270–289. [Google Scholar] [CrossRef]
  123. Elijah, O.O.; Oluwasuji, D.J. An evaluation of training needs of the nigerian construction professionals in adopting building information modelling. J. Constr. Dev. Countries 2019, 24, 63–81. [Google Scholar] [CrossRef]
  124. Liu, R.; Hatipkarasulu, Y. Introducing Building Information Modeling Course into a Newly Developed Construction Program with Various Student Backgrounds. In Proceedings of the 2014 ASEE Annual Conference & Exposition Proceedings, Indianapolis, IN, USA, 15–18 June 2014; pp. 1–8. [Google Scholar] [CrossRef]
  125. Hyarat, E.; Hyarat, T.; Al Kuisi, M. Barriers to the Implementation of Building Information Modeling among Jordanian AEC Companies. Buildings 2022, 12, 150. [Google Scholar] [CrossRef]
  126. Rani, H.A.; Al-Mohammad, M.S.; Rajabi, M.S.; Rahman, R.A. Critical Government Strategies for Enhancing Building Information Modeling Implementation in Indonesia. Infrastructures 2023, 8, 57. [Google Scholar] [CrossRef]
  127. El-Habashy, S.; Alqahtani, F.K.; Mekawy, M.; Sherif, M.; Badawy, M. Identification of 4D-BIM Barriers in Offshore Construction Projects Using Fuzzy Structural Equation Modeling. Buildings 2023, 13, 1512. [Google Scholar] [CrossRef]
  128. Oyewole, E.O.; Dada, J.O. Training gaps in the adoption of building information modelling by Nigerian construction professionals. Built Environ. Proj. Asset. Manag. 2019, 9, 399–411. [Google Scholar] [CrossRef]
  129. Broo, D.G.; Schooling, J. Digital twins in infrastructure: Definitions, current practices, challenges and strategies. Int. J. Constr. Manag. 2023, 23, 1254–1263. [Google Scholar] [CrossRef]
  130. Sasi, D.; Bhavani, R.R. Characterization of Expertise to Build an Augmented Skill Training System for Construction Industry. In Proceedings of the 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), Mumbai, India, 9–13 July 2018; pp. 116–118. [Google Scholar] [CrossRef]
  131. Panas, A.; Pantouvakis, J.P.; Lambropoulos, S. A Simulation Environment for Construction Project Manager Competence Development in Construction Management. Procedia Soc. Behav. Sci. 2014, 119, 739–747. [Google Scholar] [CrossRef]
  132. Kaiafa, S.; Chassiakos, A.P. A genetic algorithm for optimal resource-driven project scheduling. Procedia Eng. 2015, 123, 260–267. [Google Scholar] [CrossRef]
  133. Çepni, Y. BIM-Based Formwork and Cladding Quantity Take-Off Using Visual Programing. Master’s Thesis, Middle East Technical University, Ankara, Turkey, 2021. [Google Scholar]
  134. Darko, A.; Chan, A.P.; Adabre, M.A.; Edwards, D.J.; Hosseini, M.R.; Ameyaw, E.E. Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Autom. Constr. 2020, 112, 103081. [Google Scholar] [CrossRef]
  135. Alexander, B.; Ashford-Rowe, K.; Barajas-Murphy, N.; Dobbin, G.; Knott, J.; McCormack, M.; Pomerantz, J.; Seilhamer, R.; Weber, N. Horizon Report 2019 Higher Education Edition. EDU19. 2019. Available online: https://library.educause.edu/-/media/files/library/2019/4/2019horizonreport (accessed on 15 May 2023).
  136. Abuimara, T.; Hobson, B.W.; Gunay, B.; O’Brien, W.; Kane, M. Current state and future challenges in building management: Practitioner interviews and a literature review. J. Build. Eng. 2021, 41, 102803. [Google Scholar] [CrossRef]
  137. Omer, M.A.; Noguchi, T. A conceptual framework for understanding the contribution of building materials in the achievement of Sustainable Development Goals (SDGs). In Sustainable Cities and Society; Elsevier Ltd.: Amsterdam, The Netherlands, 2020; Volume 52, p. 101869. [Google Scholar] [CrossRef]
  138. Zhong, R.Y.; Peng, Y.; Xue, F.; Fang, J.; Zou, W.; Luo, H.; Thomas, S.; Lu, W.; Shen, G.Q.P.; Huang, G.Q. Prefabricated construction enabled by the Internet-of-Things. Autom. Constr. 2017, 76, 59–70. [Google Scholar] [CrossRef]
  139. Li, C.Z.; Xue, F.; Li, X.; Hong, J.; Shen, G.Q. An Internet of Things-enabled BIM platform for on-site assembly services in prefabricated construction. Autom. Constr. 2018, 89, 146–161. [Google Scholar] [CrossRef]
  140. Barro-Torres, S.; Fernández-Caramés, T.M.; Pérez-Iglesias, H.J.; Escudero, C.J. Real-time personal protective equipment monitoring system. Comput. Commun. 2012, 36, 42–50. [Google Scholar] [CrossRef]
  141. Arora, J.; Gagandeep; Kumar, R. IoT-Based Smart Home Systems. In Innovations in Computer Science and Engineering; Springer: Singapore, 2019; pp. 531–538. [Google Scholar]
  142. Francisco, A.; Asce, S.M.; Mohammadi, N.; Asce, A.M.; Taylor, J.E.; Asce, M. Smart City Digital Twin–Enabled Energy Management: Toward Real-Time Urban Building Energy Benchmarking. J. Manag. Eng. 2020, 36, 04019045. [Google Scholar] [CrossRef]
  143. Emmanuel, O.A.; Omoregie, A.D.; Koloko, A.C.O. Challenges of digital collaboration in the South African construction industry. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Bandung, Indonesia, 6–8 March 2018; pp. 6–18. [Google Scholar]
  144. Van Laar, E.; Van Deursen, A.J.; Van Dijk, J.A.; De Haan, J. The relation between 21st-century skills and digital skills: A systematic literature review. Comput. Human Behav. 2017, 72, 577–588. [Google Scholar] [CrossRef]
  145. Awolusi, I.; Marks, E.; Hallowell, M. Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Autom. Constr. 2018, 85, 96–106. [Google Scholar] [CrossRef]
  146. Çetin, S.; De Wolf, C.; Bocken, N. Circular digital built environment: An emerging framework. Sustainability 2021, 13, 6348. [Google Scholar] [CrossRef]
  147. Li, J.; Greenwood, D.; Kassem, M. Blockchain in the built environment and construction industry: A systematic review, conceptual models and practical use cases. Autom. Constr. 2019, 102, 288–307. [Google Scholar] [CrossRef]
  148. Bello, S.A.; Oyedele, L.O.; Akinade, O.O.; Bilal, M.; Delgado JM, D.; Akanbi, L.A.; Ajayi, A.O.; Owolabi, H.A. Cloud computing in construction industry: Use cases, benefits and challenges. Autom. Constr. 2021, 122, 103441. [Google Scholar] [CrossRef]
  149. Patel, T.; Guo, B.H.; Zou, Y. A scientometric review of construction progress monitoring studies. Eng. Constr. Archit. Manag. 2022, 29, 3237–3266. [Google Scholar] [CrossRef]
  150. Bademosi, F.; Blinn, N.; Issa, R.R. Use of augmented reality technology to enhance comprehension of construction assemblies. J. Inf. Technol. Constr. 2019, 24, 58–79. [Google Scholar]
  151. Bakhtawar, B.; Thaheem, M.J.; Arshad, H.; Tariq, S.; Mazher, K.M.; Zayed, T.; Akhtar, N. A sustainability-based risk assessment for p3 projects using a simulation approach. Sustainability 2022, 14, 344. [Google Scholar] [CrossRef]
  152. Azhar, S.; Khalfan, M.; Maqsood, T. Building information modeling (BIM): Now and beyond. Australas J. Constr. Econ. Build. 2012, 12, 15–28. [Google Scholar] [CrossRef]
  153. Khahro, S.H.; Kumar, D.; Siddiqui, F.H.; Ali, T.H.; Raza, M.S.; Khoso, A.R. Optimizing energy use, cost and carbon emission through building information modelling and a sustainability approach: A case-study of a hospital building. Sustainability 2021, 13, 3675. [Google Scholar] [CrossRef]
  154. Li, X.; Yi, W.; Chi, H.L.; Wang, X.; Chan, A.P. A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Autom. Constr. 2016, 86, 150–162. [Google Scholar] [CrossRef]
  155. Austin, M.; Delgoshaei, P.; Coelho, M.; Heidarinejad, M. Architecting Smart City Digital Twins: Combined Semantic Model and Machine Learning Approach. J. Manag. Eng. 2020, 36, 04020026. [Google Scholar] [CrossRef]
  156. Lu, Q.; Xie, X.; Parlikad, A.K.; Schooling, J.M. Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Autom. Constr. 2020, 118, 103277. [Google Scholar] [CrossRef]
  157. Ibrahim FS, B.; Ebekozien, A.; Khan PA, M.; Aigbedion, M.; Ogbaini, I.F.; Amadi, G.C. Appraising fourth industrial revolution technologies role in the construction sector: How prepared is the construction consultants? Facilities 2022, 40, 515–532. [Google Scholar] [CrossRef]
  158. Abdelmegid, M.A.; González, V.A.; Poshdar, M.; O’Sullivan, M.; Walker, C.G.; Ying, F. Barriers to adopting simulation modelling in construction industry. Autom. Constr. 2020, 111, 103046. [Google Scholar] [CrossRef]
  159. Seidu, R.D.; Young, B.E.; Clack, J.; Adamu, Z.; Robinson, H. Innovative changes in Quantity Surveying Practice through BIM, Big Data, Artificial Intelligence and Machine Learning. Appl. Sci. Univ. J. Nat. Sci. 2020, 4, 37–47. [Google Scholar]
  160. Moselhi, O.; Alshibani, A. Crew optimization in planning and control of earthmoving operations using spatial technologies. Electron. J. Inf. Technol. Constr. 2007, 12, 121–137. [Google Scholar]
  161. Zayed, T.M.; Halpin, D.W. Pile construction productivity assessment. J. Constr. Eng. Manag. 2005, 131, 705–714. [Google Scholar] [CrossRef]
  162. Hsu, P.Y.; Angeloudis, P.; Aurisicchio, M. Optimal logistics planning for modular construction using two-stage stochastic programming. Autom. Constr. 2018, 94, 47–61. [Google Scholar] [CrossRef]
  163. Liu, J.; Lu, M. Constraint Programming Approach to Optimizing Project Schedules under Material Logistics and Crew Availability Constraints. J. Constr. Eng. Manag. 2018, 144, 04018049. [Google Scholar] [CrossRef]
  164. Hwang, S.I.; Son, J.H.; Lee, S.H. Development of scheduling model for earth work using genetic algorithm. KSCE J. Civ. Eng. 2014, 18, 1618–1624. [Google Scholar] [CrossRef]
  165. Li, H.; Zhang, C.; Liu, Y.; Arditi, D.; Xu, C.; Shim, E. Academia and Industry Perceptions of Construction Planning and Scheduling Education. J. Civ. Eng. Educ. 2022, 148, 04022005. [Google Scholar] [CrossRef]
  166. Klaess, J. Manufacturing Skills: What Skills Do Manufacturers Need Most in 2020? Tulip. 2019. Available online: https://tulip.co/blog/what-skills-do-manufacturers-need-most-in-2020/ (accessed on 30 September 2023).
  167. Leitão, P.; Geraldes, C.A.; Fernandes, F.P.; Badikyan, H. Analysis of the workforce skills for the factories of the future. In Proceedings of the 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 9–12 June 2020; Volume 1, pp. 353–358. [Google Scholar]
  168. Azmat, F.; Ahmed, B.; Colombo, W.; Harrison, R. Closing the Skills Gap in the Era of Industrial Digitalisation. In Proceedings of the 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 9–12 June 2020; pp. 365–370. [Google Scholar] [CrossRef]
  169. Akyazi, T.; del Val, P.; Goti, A.; Oyarbide, A. Identifying Future Skill Requirements of the Job Profiles for a Sustainable European Manufacturing Industry 4.0. Recycling 2022, 7, 32. [Google Scholar] [CrossRef]
  170. Jurczuk, A.; Florea, A. Future-Oriented Digital Skills for Process Design and Automation. Hum. Technol. 2022, 18, 122–142. [Google Scholar] [CrossRef]
  171. Salminen, K.; Siivonen, J.; Hillman, L.; Rainio, T.; Ukonaho, M.; Ijas, M.; Lantz, M.; Aho, M. Sustainable Digital Transformation of Manufacturing Industry: Needs for Competences and Services Related to Industry 5.0 Technologies. In Proceedings of the 2023 Portland International Conference on Management of Engineering and Technology (PICMET), Monterrey, Mexico, 23–27 July 2023; pp. 1–9. [Google Scholar] [CrossRef]
  172. Li, G.; Yuan, C.; Kamarthi, S.; Moghaddam, M.; Jin, X. Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis. J. Manuf. Syst. 2021, 60, 692–706. [Google Scholar] [CrossRef]
  173. Umachandran, K.; Corte, V.D.; Amuthalakshmi, P.; Ferdinand-James, D.; Said MM, T.; Sawicka, B.; Del Gaudio, G.; Monah, T.R.; Craig, N.; Aravind, V.R.; et al. Designing learning-skills towards industry 4.0. World J. Educ. Technol. Curr. Issues 2019, 11, 150–161. [Google Scholar]
Buildings 13 02711 g001
Figure 1. Flowchart of research methodology.
Figure 1. Flowchart of research methodology.
Buildings 13 02711 g001
Buildings 13 02711 g002
Figure 2. PRISMA flowchart.
Figure 2. PRISMA flowchart.
Buildings 13 02711 g002
Buildings 13 02711 g003
Figure 3. Number of publications.
Figure 3. Number of publications.
Buildings 13 02711 g003
Buildings 13 02711 g004
Figure 4. Publication trends.
Figure 4. Publication trends.
Buildings 13 02711 g004
Buildings 13 02711 g005
Figure 5. Co-authorship network of all authors.
Figure 5. Co-authorship network of all authors.
Buildings 13 02711 g005
Buildings 13 02711 g006
Figure 6. Co-authorship network of connected authors.
Figure 6. Co-authorship network of connected authors.
Buildings 13 02711 g006
Buildings 13 02711 g007
Figure 7. Co-occurrence network of keywords.
Figure 7. Co-occurrence network of keywords.
Buildings 13 02711 g007
Table 1. Frequency of publication outlets.
Table 1. Frequency of publication outlets.
OutletCount of Publications
ASEE Annual Conference & Exposition4
Buildings3
Journal of Construction in Developing Countries2
International Journal of Construction Management2
Journal of Management in Engineering2
RICS Construction and Building Research Conference2
IEEE International Conference on Emerging eLearning Technologies and Applications (ICETA)2
International Conference of Education, Research and Innovation (ICERI)2
Journal of Civil Engineering Education1
International Journal of Construction Education and Research1
Nanotechnologies in Construction A Scientific Internet-Journal1
Frontiers in Built Environment1
IOP Conference Series: Earth and Environmental Science1
IEEE International Conference on Advanced Learning Technologies (ICALT)1
Journal of Engineering, Design and Technology1
Australasian Association for Engineering Education (AAEE) Annual Conference1
Engineering Management Journal1
Industry and Higher Education1
American Society of Civil Engineers (ASCE) Construction Research Congress1
Infrastructures1
IOP Conference Series: Materials Science and Engineering1
Routledge, Taylor & Francis Group, Informa UK Limited1
Built Environment Project and Asset Management1
South African Journal of Science1
EAI/Springer Innovations in Communication and Computing1
Transportation Research Record: Journal of the Transportation Research Board1
Procedia—Social and Behavioral Sciences1
AIP Conference Proceedings1
Engineering, Construction and Architectural Management1
International Conference on Intellectual Capital, Knowledge Management and Organisational Learning (ICICKM)1
Sustainability1
International Conference on Computers in Education (ICCE)1
World Congress on Engineering (WCE)1
International Conference on Education and New Learning Technologies (EDULEARN)1
International Conference on Industrial Engineering and Operations Management1
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Siddiqui, F.H.; Thaheem, M.J.; Abdekhodaee, A. A Review of the Digital Skills Needed in the Construction Industry: Towards a Taxonomy of Skills. Buildings 2023, 13, 2711. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13112711

AMA Style

Siddiqui FH, Thaheem MJ, Abdekhodaee A. A Review of the Digital Skills Needed in the Construction Industry: Towards a Taxonomy of Skills. Buildings. 2023; 13(11):2711. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13112711

Chicago/Turabian Style

Siddiqui, Fida Hussain, Muhammad Jamaluddin Thaheem, and Amir Abdekhodaee. 2023. "A Review of the Digital Skills Needed in the Construction Industry: Towards a Taxonomy of Skills" Buildings 13, no. 11: 2711. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13112711

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop