Next Article in Journal
Assessing Green Practices on Eco-Friendly Hotel Customer Loyalty: A Partial Least Squares Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis Hybrid Approach
Previous Article in Journal
Indigenous or Exotic Crop Diversity? Which Crops Ensure Household Food Security: Facts from Tanzania Panel
Previous Article in Special Issue
Implementation of Integrated Life Cycle Design Principles in Ground Improvement and Piling Methods—A Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 9
10.3390/su16093824
sustainability-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

Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review

by
Chuyou Fu
1,
Jun Wang
2,*,
Ziyi Qu
2,
Martin Skitmore
3,
Jiaxin Yi
4,
Zhengjie Sun
5 and
Jianli Chen
6
1
School of Public Administration and Humanities, Dalian Maritime University, Dalian 116026, China
2
School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China
3
Faculty of Society and Design, Bond University, Robina, QLD 4226, Australia
4
Yishui Campus, Linyi University, Linyi 276400, China
5
School of Accounting, Southwestern University of Finance and Economics, Chengdu 611130, China
6
Department of Civil Engineering, University of Utah, Salt Lake City, UT 84112, USA
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(9), 3824; https://doi.org/10.3390/su16093824
Submission received: 12 April 2024 / Revised: 29 April 2024 / Accepted: 30 April 2024 / Published: 2 May 2024
(This article belongs to the Special Issue Sustainable Construction Engineering Processes)
Browse Figures
Versions Notes

Abstract

:
Considering the emergence and adoption of various innovative technologies, the construction industry has undergone transformation into a more secure, highly efficient, and ecologically sustainable landscape. An increasing number of studies uses the structural equation modeling (SEM) method to explore the dynamics of technology adoption and use within the construction sector. Previous studies have mainly focused on qualitative analysis using the SEM method to analyze technology adoption and usage in the construction industry. This study, however, distinguishes itself from previous research by focusing on the SEM method itself and conducting a systematic analysis using scientometric methods. Based on a total of 140 relevant journal articles, this study adopts a scientometric analysis approach to conduct a holistic review encompassing sources, researchers, keywords, and highly cited documents. The research findings are as follows: (1) the primary focus of the current research topics is on BIM technology; (2) most studies employ cross-sectional SEM instead of longitudinal SEM; (3) there is a deficiency in the theoretical foundation for designing SEM in current research; and (4) the selection of either reflective or formative measures lacks sufficient rigor. Qualitative analysis is used to examine prevailing issues in research design and address the intricate technicalities and potential challenges inherent in the SEM method. Three research gaps and future directions are presented: diversifying regions of study and research topics, incorporating theoretical support for research design, and carefully choosing reflective or formative measures. The findings provide a comprehensive roadmap and valuable reference for future research in this field.

1. Introduction

The construction industry, as a key pillar industry of a country or region, faces numerous pressures and challenges in the modern era. Such frequent issues as severe resource waste, low production efficiency, environmental contamination, and occupational safety incidents have jeopardized the construction industry’s long-term development [1]. Emerging technologies (tools, modifications, or equipment that can help construction practitioners achieve certain goals, carry out specific tasks, or overcome new obstacles) have thus been regularly incorporated into design and construction practices [2,3]. Both promote the construction industry’s healthy and sustainable development [4,5] and help practitioners improve.
Significant research has been conducted to date on adopting and using various technologies in the construction sector to create a safer, more productive, and more sustainable working environment. Wearing sensing devices, for example, can significantly improve the safety of front-line construction workers [6]. Promoting the use of social media [7] and building information modeling (BIM) [8] can boost construction productivity significantly. In the road construction industry, intelligent compaction (IC) or ground penetrating radar (GPR) applications can help quality managers improve job efficiency and quality [9]. Adopting green building technology (GBTS) is an important step toward long-term environmental, economic, and social sustainability in the construction industry [10]. Three-dimensional printing [11] and cloud computing [12] technology can reduce costs, preserve resources, develop industry production processes, and eliminate over-reliance on labor.
Structural equation modeling (SEM) has been used in an increasing number of studies due to its greater ability to estimate effects. Compared to other methods, SEM is a useful technique for assessing complex theoretical relationships among multiple variables [13], widely applied in the field of construction for technology adoption. Ahmed et al. use partial least squares (PLS)—SEM in analyzing questionnaires distributed to 1200 construction companies to study the impact of interoperability-related factors, determinants, and barriers in adopting BIM technology in Malaysia’s construction industry [14]. PLS-SEM is used by Al-Hashmy et al. to investigate the impact of a computerized accounting information system (CAIS) on construction firm performance, finding that most of it is mediated by innovation [15]. Etemadi et al. investigate the factors influencing architecture professionals’ use of social media for work-related knowledge sharing using covariance-based (CB)—SEM [7].
SEM has the advantage of not requiring all variables to be observable and not assuming that all variables are error-free measurements [16,17]. It can model and estimate the complex relationship between multiple dependent variables and independent variables at the same time, use multiple indicators to indirectly measure the unobservable concepts under consideration, and account for the estimation relationship and measurement errors of observed variables (OVs) [17,18]. When dealing with structures and investigating mediating and regulating effects, SEM is frequently superior to multiple regression [19]. Furthermore, the study of technology adoption and use is essentially social research because it involves the analysis of people’s behavioral intentions [20], and dealing with the difficulty of quantifying such social science issues as human motivation, perception, and attitude is often the major advantage of using SEM [21]. This makes it a commonly chosen technique for analyzing technology adoption and use in both the private and public sectors [22].
Despite the increasing popularity of using SEM to analyze technology adoption and use in the construction sector [7,14,15,23], existing publications can be expanded or improved. Most studies use the SEM method to assess the impact of multiple variables on technology adoption in the construction field, but little attention has been paid to SEM itself, and there are many issues in the design of the model. Essential rules are sometimes violated or ignored [24,25]. For example, the principle of using 10 times the maximum number of paths for any structure in the external model [26] is not always followed. Furthermore, despite the multivariate normality of data being a required assumption for CB-SEM, some studies fail to test and report it, resulting in such problems as an inflated goodness of fit [27]. Furthermore, theories should be used to define individual constructs. All latent variables (LVs) should be present in a hypothesized model [28,29,30,31]. Some studies, however, do not provide adequate theoretical support for selecting measurement items and developing appropriate hypotheses. As a result, a comprehensive and critical review is required to summarize existing research, identify common errors, and direct future work using SEM to examine technology adoption and use in the construction industry.
Previous reviews [2,32,33,34,35,36,37,38] have contributed significantly to the current body of knowledge. Despite its growing popularity, they do not pay attention to SEM. Furthermore, most review articles are manual and qualitative, with only a few employing a scientometric analysis approach to conduct a systematic approach. According to recent research, humans are better at acquiring and interpreting domain knowledge when presented in graphical formats [39,40]. To bring the situation up to date, the present study employs the science mapping approach, which uses graphical representation to reveal the inherent relationships involved. Science mapping is a powerful bibliometric technique used to understand and monitor the structure and evolution of the SEM-related research field in order to identify the relationships among authors, disciplines, and studies [41]. To supplement previous qualitative work, this approach allows to conduct a quantitative analysis for discovering technology applications in the construction sector using SEM. This study offers several contributions: It aids scholars in achieving a comprehensive understanding of the literature direction concerning the use of SEM methods in the field of construction technology adoption and usage, identifies emerging and promising topics within the existing knowledge framework, and uncovers issues in current research that utilizes SEM methods, thereby facilitating improvements.
This review has the following specific research objectives, with a clear scope of focusing on studies that use the SEM approach to discover technology adoption and use in the construction industry: (1) analyzing sources, keywords, authors, and articles using a science mapping approach; (2) analyzing research works from both the research design and SEM technique perspectives; and (3) exposing existing research gaps and determining potential future research areas.

2. Research Methods

This section outlines the three-step review approach, which consists of a literature review, scientometric analysis, and a qualitative discussion. Quantitative analysis using scientometric methods quantifies the application of SEM in the field of construction technology adoption, while qualitative analysis provides current research references for relevant scholars. A comprehensive literature review assists researchers in related fields to better understand the development trends, existing issues, and future expectations of the topic. Figure 1 depicts the entire research methods procedure.

2.1. Literature Search

The literature search was carried out in three widely used academic databases (Web of Science, Scopus, and Engineering Village) using the query “(structural equation modeling) AND (construction industry) AND (technology)” to limit the topic to the use of SEM to reveal technology adoption in the construction industry. After narrowing the search criteria to journal articles published in English, a total of 2323 initial documents were identified. Duplicated documents were deleted, and the remaining article titles and abstracts were manually evaluated for further screening. This resulted in the elimination of items referring to industries other than construction (e.g., the chemical, manufacturing, shipbuilding, electrical and electronic, and automotive sectors), methods other than the SEM technique (e.g., Delphi, analytic hierarchy process, linear regression, and machine learning), and topics other than technology adoption and use (e.g., safety behavior, contractor selection, knowledge management, effective communication, and social interaction). Ultimately, 140 journal articles were chosen to form the literature sample for analysis.

2.2. Scientometric Analysis

The review’s second stage includes scientometric analysis, a popular method for domain analysis and visualization [42]. This has been widely used to support systematic literature reviews in various aspects of building construction research (e.g., [43,44,45,46,47]). VOSViewer is a text-mining tool that generates network-based visualizations using distance metrics. Each node in the network can represent various data points, such as the source journal, author, organization, country, and keyword [48,49]. According to van Eck and Waltman, the proximity between nodes represents their degree of closeness, which can be quantified using various metrics such as co-authorship, shared references, and co-occurrence [50]. The 140 articles are entered into VOSViewer (1.6.20) to conduct the scientometric analysis, allowing the generation of results relating to the impact of journals, keywords, researchers, and articles in applying SEM to uncover technology adoption and use in the construction industry.

2.3. Qualitative Discussion

Finally, a thorough qualitative discussion is held from various perspectives. The first section examines research design issues, such as the study’s geographical scope and central focus, choice between cross-sectional and longitudinal study designs, and prevalent use of theoretical frameworks. The section on SEM techniques that follows discusses the technical complexities and potential pitfalls. This includes deciding between CB-SEM and PLS-SEM methodologies, distinguishing between reflective and formative measures, emphasizing the importance of theoretical foundations for both structural and measurement models, and debating mediation and moderation effects in SEMs. The final section identifies research gaps and potential directions for future research.

3. Results

This section presents the findings of science mapping based on the 140 articles which include journal analysis, researcher analysis, keyword analysis, and document analysis using VOSViewer.

3.1. Journal Source Analysis and Researcher Analysis

Figure 2 depicts journal sources as nodes, with the size of each node corresponding to the number of associated publications. The distance between nodes roughly reflects the extent to which they are cross-referenced [50], and the color of each node represents the clustering results, which is automatically determined by VOSViewer using the smart local moving average algorithm [22,49,50]. In terms of the number of articles, the visualizations show that Engineering Construction and Architectural Management (18 documents) as well as Sustainability (18 documents) make the most significant contributions, followed by the Journal of Construction Engineering and Management (12 documents) and Buildings (11 documents).
Table 1 summarizes the quantitative metrics of most productive journals, highlighting the highest total citation counts for Automation in Construction, Engineering Construction and Architectural Management, Sustainability, Journal of Management in Engineering, and Journal of Construction Engineering and Management. A normalized citation metric is introduced to counter the potential bias of older documents receiving more citations. This is obtained by dividing a document’s citation count by the average citation count of all documents using VOSViewer [48]. The recipients of the most normalized citations are Engineering Construction and Architectural Management and Sustainability.
Researchers are producers of knowledge, and a thorough analysis of researchers’ collaborative network relationships as well as countries can help explore their related research efforts to capture the application domains of SEM in the construction field, especially in recent dominant areas.
Figure 3 depicts the number of articles (≥2) published by individual researchers. Each researcher is represented as a node in this diagram, with the node’s size corresponding to the number of citations the researcher has received. The distance between nodes represents the number of times two researchers have cited each other. This visual representation identifies the researchers who have received the most citations, including Seulki Lee (4 documents, 240 citations), Jungho Yu (4 documents, 240 citations), Changwan Kim (2 documents, 231 citations), Hyojoo Son (2 documents, 231 citations), and Nicholas Chileshe (3 documents, 194 citations). Table 2 summarizes the quantitative metrics of the highly cited researchers, with Nicholas Chileshe and Ahmed Kineber emerging as the top normalized citation recipients.
Figure 4 illustrates the collaboration network among researchers from different geographical regions, where each region is represented by a node, and the connections between nodes reflect the collaboration relationships between regions. The thickness of the connections between nodes reflects the strength of collaboration between the corresponding publications. In terms of productivity and impact, Australia (26 documents, 869 citations), China (51 documents, 640 citations), and South Korea (11 documents, 591 citations) are the top-producing regions. This indicates their significant contributions to enriching and advancing the application of SEM in the adoption of technology in the construction field. In terms of collaboration strength, researchers from Malaysia, Nigeria, and Saudi Arabia have the strongest collaboration relationships with researchers from other geographical regions.

3.2. Researchers, Keywords, and Document Analysis

Keywords are short phrases that represent the core content of the article. Keyword analysis is crucial because scientific publications contain valuable textual information that can represent the primary interests and hotspots of specific fields [51]. This analysis takes into account both Author Keywords (keywords that are manually entered by authors) and Keywords Plus (keywords that are automatically suggested by programs). To ensure consistency, manual text data preprocessing is used to standardize the writing formats of various semantically similar expressions. For example, terms such as ‘building information modeling (BIM)’, ‘BIM’, and ‘building information modeling’ are all treated as synonymous with ‘BIM’.
Following the establishment of a minimum keyword occurrence threshold of three in VOSViewer, 120 out of 711 keywords are chosen and visualized in Figure 5. Nodes with bigger sizes signify keywords that appear more frequently. As indicated in the timeline legend, the average year of occurrence is represented by the node color, and the distance between nodes generally reflects how frequently two terms appear together. Notably, the top 10 most frequently occurring keywords are ‘bim’ (44), ‘adoption’ (38), ‘structural equation modeling’ (38), ‘technology’ (36), ‘management’ (36), ‘construction industry’ (29), ‘pls-sem’ (27), ‘implementation’ (26), ‘construction’ (22), ‘user acceptance’ (21), ‘barriers’ (21), and ‘information technology’ (18). The number of times each keyword appears is indicated in parentheses.
Finally, the articles are used to analyze the literature sample. Figure 6 depicts the 71 documents obtained via VOSViewer, with at least five citations per article. Each node in this representation stands for a specific article, and the size of the node reflects the overall number of citations that article has received. The closer the nodes are, the more related they are, with the distance between them roughly reflecting the frequency at which they cite each other.
Table 3 shows the titles and citation counts for the 20 most-referenced documents, arranged in descending order by citation number. The frequency of citations underscores the enduring popularity of BIM in using SEM [52,53,54,55,56,57,58,59,60,61,62,63].

4. Qualitative Discussion

This section offers a thorough qualitative assessment of research design perspectives, SEM techniques, research gaps and future directions, which comes after the scientometric analysis and outcomes in Section 3.

4.1. Research Design

4.1.1. Regions of Study

When surveys and questionnaires are the primary means of data collection for SEM, the region frequently plays a pivotal role in influencing social research outcomes. Among the 140 articles in the sample, 138 explicitly stated the regions from which their data were gathered, with the vast majority (134 articles) focusing on a single region. Within this subset of 134 articles focused on specific regions, China (42 articles) received the most attention, followed by Nigeria (20), Malaysia (14), Australia (9), South Korea (7), Ghana (5), Hong Kong (4), India (3), Iran (3), and South Africa (3). Only 4 of the 140 articles [57,68,71,72] contained research conducted across multiple regions.
Several highly cited studies explicitly stated that their research findings should be interpreted within the specific regions where the data were collected, emphasizing the difficulty in generalizing their findings due to the research’s region-specific nature [52,55,56,57,59,61,62,63,65,70,71].

4.1.2. Research Topics

BIM has become a focal point of study due to its increasing popularity and widespread use throughout the project life cycle. Of the 140 articles reviewed, 64 are primarily concerned with BIM from two perspectives: investigating the major factors, barriers, and drivers influencing BIM adoption among various stakeholders (e.g., owners, architects, engineers, contractors) at various organizational levels (e.g., large, medium, or small) and across diverse regions; and evaluating the impact of BIM usage on such emerging areas as sustainable building construction [73,74,75,76,77], project performance [78,79], and the construction supply chain [80,81]. The remaining frequently studied topics are digitalization (9 articles), information communication technologies (ICT) (8), social media (6), robotic automation (5), blockchain (4), cloud technology (4), 3D printing (3), cyber technology (3), green building technology (3), internet of things (IoT) (3), virtual reality or augmented reality (VR/AR) (3), and web-based training or management systems (WBTMS) (3).
In the construction industry, the adoption of various technologies—including BIM, 3D printing, and IoT—is driven by the need to enhance productivity and efficiency, improve quality, and meet market demands. Scholars frequently utilize SEM methods to investigate the factors influencing the adoption of these technologies and to assess the beneficial impacts of SEM methods on the adoption practices across construction projects of varying scales. This growing application of SEM methods in the construction sector underscores their relevance and utility. Nevertheless, numerous studies still face significant challenges in designing SEM methods effectively.

4.1.3. Cross-Sectional vs. Longitudinal Studies

Cross-sectional SEM is used to examine data collected at a single point in time, focusing on the relationships between variables at that time [82], while longitudinal SEM analyses data collected over multiple time points—most commonly to investigate how variables evolve or to evaluate causal relationships that may emerge over time [83]. While cross-sectional studies can establish associations between variables, it is difficult to determine whether these associations reflect true causal relationships. In such cases, incorporating time lags into the research design becomes critical, indicating the need for a longitudinal component [24]. Only 2 of the 140 articles were longitudinal studies. In comparison, 23 of the remaining 138 articles acknowledged their cross-sectional nature’s limitations. These recognize the importance of future research incorporating longitudinal studies to further expose the causal relationships between variables in their models.

4.1.4. Theoretical Frameworks and Key Constructs

Many theories and models have been developed to help understand the rationale for accepting or rejecting new technology in various industries, including construction. Within the literature sample, five theoretical models stand out: the Technology Acceptance Model (TAM) [84], Technology–Organization–Environment (TOE) [85] theory, Theory of Planned Behavior (TPB) [86], Innovation Diffusion Theory (IDT) [87], and Unified Theory of Acceptance and Use of Technology (UTAUT) [88], utilized in thirty-one, eight, seven, five, and five articles, respectively. These theoretical frameworks have aided researchers in identifying key constructs and developing causality hypotheses, thereby advancing the evolution of information system theories and behavioral sciences, particularly through the construction industry lens. Table 4 summarizes the main concepts and descriptions of each commonly used theory.

4.2. SEM Techniques

SEM uses diverse model types to delineate relationships between observed variables, aiming to quantitatively test theoretical hypotheses. Fundamentally, SEM combines features of path models with confirmatory factor models, enabling the simultaneous inclusion of both latent and observed variables. SEMs are categorized into two types: the structural model, which focuses on relationships between latent variables, and the measurement models, which relate latent variables to their observed indicators [93].

4.2.1. CB-SEM vs. PLS-SEM

Two types of SEMs are commonly used in construction-related research: CB-SEM and PLS-SEM. These are complementary but differ in estimation methodology, data distribution assumptions, and research objectives. CB-SEM, for example, uses methods such as maximum likelihood to estimate model parameters based on OV covariance matrices [94]. As a result, CB-SEM necessarily assumes that data follow a multivariate normal distribution. On the other hand, PLS-SEM employs the partial least squares technique to estimate model parameters by maximizing the OVs’ explained variance [17], which is less sensitive to data distribution assumptions and provides greater robustness [95]. Furthermore, CB-SEM is better suited for confirmatory analysis aimed at testing theories, while PLS-SEM is more suited to exploratory analysis, especially when dealing with smaller sample sizes [95].
Figure 7 depicts the timeline-based choice between CB-SEM and PLS-SEM in the reviewed literature. Only 1 of the 140 articles in the sample failed to specify the method. The graph shows a growing trend of using SEM, while PLS-SEM is shown to be more popular than CB-SEM, especially in 2022 and 2023.
In total, 23 of the 140 articles did not specify the software programs used to build SEM. The following software tools were frequently used in the remaining 117 articles: for CB-SEM, AMOS [96] in 46 articles and LISREL [97] in 5; and for PLS-SEM, SmartPLS [98] in 62 articles, with PLS Graph and Warp PLS each used in 2 articles. PLS-SEM was preferred over CB-SEM for a variety of reasons, the most common of which is its ability to handle non-normally distributed data (mentioned 28 times), suitability for exploratory studies (26 times), better handling of small sample sizes (19 times), ability to accommodate formative constructs (5 times), and superior predictive capabilities (3 times). It is worth noting that even though CB-SEM requires data to conform to multivariate normality, only 10 of the 59 CB-SEM studies disclosed that they conducted a normality test on their collected data. Most of these studies either omitted or provided insufficient disclosure about these tests, although failing to satisfy this assumption can lead to overestimating the model’s goodness of fit [27].

4.2.2. Reflective vs. Formative Measures

Within the measurement model, two potential relationships exist between the OVs and the LV: reflective and formative. Variations in the LV cause covariance among OVs in reflective measures, establishing a causal direction from the LV to the Ovs [99]. As a result, because they all manifest the same latent construct, OVs are expected to have correlations. Evaluating the construct validity of reflective measures, including reliability assessments, convergent validity, and discriminant validity [100], is often critical. On the other hand, formative measures contend that OVs collectively influence changes in the LV, thereby establishing a causal pathway from the OVs to the latent construct [99]. In contrast to reflective measures, the expectation for correlations among OVs in formative models is less stringent because they may or may not be correlated. In addition, multicollinearity among OVs can pose a significant challenge and should therefore be tested with such measures as the variance inflation factor [25]. Figure 8 depicts a graphical representation of both reflective and formative measures.
A total of 3 of the 140 articles reviewed built first-order formative measurement models. Furthermore, six articles used first-order reflective models but second-order formative measurement models. The remaining 131 articles only built reflective measures. However, in all the remaining cases, the assumption that the relationships between LVs and OVs are inherently reflective raises concerns. Some articles, for example, developed a reflective measure that linked job satisfaction (the LV) to various OVs, such as job security, ability development, income level, and worker safety. However, whether a formative measure is more appropriate is debatable because each OV captures different aspects or attributes of job satisfaction. As a result, they are not always related. The true causal relationship between OVs and LVs needs to be carefully considered because the choice of reflective versus formative measures has significant implications for overall goodness of fit in the context of measurement model misspecification [98]. Although traditional CB-SEM is well known for its compatibility with reflective measures [24], it is also important to note that formative measures can be integrated into CB-SEM [101]. In addition, given the emergence and growing popularity of PLS-SEM, researchers should feel more encouraged to include formative measures in their future work. Table 5 summarizes the differences between reflective and formative measures.

4.2.3. Theories for Structural and Measurement Models

Theories at the structural and measurement model levels should be used as the primary foundation in SEMs to hypothesize causal relationships and define individual constructs [28,29,30,31]. A causal relationship in hypotheses that lack proper justification or theoretical foundations can be undermined [102]. As a result, hypothesized causal relationships in structural models need to be articulated transparently, based on theoretical support, previous research, scientific knowledge, logical reasoning, or other empirical evidence [103]. Furthermore, using factor analysis, whether exploratory or confirmatory, within LV measurement models requires a solid theoretical foundation that reveals prior knowledge about the OVs associated with LVs [104]. Without this theoretical underpinning, factor analysis risks becoming nothing more than a dimensionality reduction tool. To ensure accurate interpretation of LVs, SEM requires measurement models to be grounded in prior knowledge. Based on established theories, studies should also examine the relationships between OVs and LVs [105,106].
Following the literature review, it was clear that 30 of the 59 CB-SEM articles lack adequate theoretical support for their measurement models, and 28 lack the necessary theoretical foundations for their structural models. Similarly, 24 of the 80 PLS-SEM articles lack theoretical support for their measurement models, and 43 face a similar shortfall in theoretical support for their structural models. In summary, 38% of the reviewed articles need more theoretical support for their measurement models, while 51% need more theoretical support for their structural models.
Furthermore, 54 of the 140 articles in the sample contain models with an OV:LV ratio of less than three. This is significant because Kline’s three-variable principle recommends a minimum of three OVs to adequately identify a measurement model. Models with single-indicator constructs representing a single LV with only one OV are included in five articles [82]. However, single-indicator constructs are only considered appropriate when the OV perfectly represents the LV [24]. Using such constructs is typically risky because they perform less effectively than multi-item constructs in most scenarios [107].
Finally, because CB-SEM is frequently associated with theory testing and confirmation tasks, it is critical to summarize, delineate, and effectively integrate pre-existing theories concisely. Although PLS-SEM is particularly useful for promoting theory development, it is still useful to encapsulate theories that can provide partial support for the hypotheses within the structural model and aid in selecting measurement items, thereby contributing to the provision of theoretical support.

4.2.4. Mediation and Moderation Effects

Path analysis was created to quantify the relationships between multiple variables and effectively test and develop structural hypotheses of mediation and moderation effects [31]. In SEM, the mediation effect assumes that an LV can influence another LV directly and indirectly through a third LV (the mediator) [103]. In contrast, the moderation effect investigates how a third LV (the moderator) influences the relationship between two LVs, typically through interaction [108]. Mediators and moderators are frequently used in research design, especially when dealing with complex and unresolved issues in theory development [24]. Identifying and quantifying mediation and moderation effects can significantly contribute to the existing body of knowledge, making these variables focal points in research design across a wide range of scenarios [109]. Furthermore, as situations become more complex, the need to understand mediated moderation and moderated mediation effects becomes more apparent [108].
Only four articles in the sample examined and discussed the moderation effect. For example, Yang et al. find that team relationships moderate the relationship between knowledge management and project success [67]. According to Jia et al., formal knowledge governance moderates the relationship between learning inertia and BIM integration intention [110]. Furthermore, Yang and Huang find that the platform type (traditional vs. cloud-based) moderates the relationship between information platform usage and construction capability [111]. Furthermore, Jiang et al. find that team member support moderates the relationship between the connective use of mobile information and communication technology (MICT) and the technology–work conflict [112].
In terms of mediation effects, a larger subset of 19 out of 61 articles in the sample that included mediation effects in their structural models conducts tests and discusses these mediation effects.

4.3. Research Gaps and Future Directions

While using SEM methods to investigate technology adoption and usage in the construction industry is becoming more popular, it is critical to recognize and address several limitations. This recognition of limitations can aid in identifying research gaps and defining future research directions.

4.3.1. Diversifying Region of Study and Research Topic

A sizable portion of existing research focuses on developing regions, with China alone accounting for 30% of all reviewed articles. Surprisingly, fewer studies relate to developed regions than those of China. As a result, there is a clear need for future research to delve into the adoption and utilization of technology in other prominent construction markets, such as the United States, India, Japan, Indonesia, and Australia, especially given the difficulty in generalizing findings from region-specific studies to other contexts. Furthermore, researchers are encouraged to conduct more comprehensive multi-region studies, allowing for a more robust comparison of technology adoption and usage patterns across different regions.
In terms of research topics, BIM has taken the lead, accounting for 45% of all articles reviewed. This is largely due to its growing popularity and proven benefits, which have attracted the interest of researchers and industry professionals. Given the construction industry’s increasing digitization, researchers are encouraged to investigate other emerging and underutilized technology categories such as drones, digital twins, IoT, blockchain, 3D printing, and AR/VR. This encouragement is especially important because many of these technologies can be integrated with BIM to improve the design and construction processes further.

4.3.2. Incorporating Theoretical Support for Research Design

Even though numerous theories have been developed to analyze the adoption and utilization of technology across various industries, construction industry research continues to face significant challenges. This stems from the lack of well-founded theories at both the structural and measurement model levels. To ensure the trustworthiness and interpretability of research findings, it is critical that hypothesized causal relationships be firmly rooted in theoretical underpinnings and measurement models anchored in existing knowledge when using SEM in research. However, 51% of the reviewed articles lacked adequate theoretical support to establish structural models, and 38% lacked the necessary theoretical foundation for their measurement models. As a result, researchers are strongly encouraged to review and incorporate existing theories to aid in formulating hypothetical causal relationships and selecting appropriate measurement indicators, particularly when employing the CB-SEM method, which well-established theoretical frameworks should ideally support. A thorough examination and appropriate incorporation of existing theories may also aid in avoiding the use of single-indicator constructs.

4.3.3. Carefully Choosing Reflective or Formative Measures

Because each type of measure is subject to different rationalities and statistical tests, the choice between reflective and formative measures should be based primarily on the causal relationships between LVs and OVs. Incorrectly configuring the measurement model can significantly impact the overall model’s goodness of fit. Currently, the vast majority of studies use reflective measures. Nevertheless, upon a comprehensive examination of these reflective studies, situations emerge where formative measures might be more suitable when each OV captures distinct LV attributes. As a result, these OVs are not necessarily intercorrelated. Given the PLS-SEM method’s growing popularity, researchers should feel less hesitant about incorporating formative measures. In essence, they should carefully deliberate on the relationships between the LV and its OVs, recognizing that formative measures may be more appropriate in some contexts.

5. Conclusions

This study adopts a three-stage approach, systematically reviewing 140 journal articles that utilize SEM to investigate technology adoption and usage in the construction industry, employing scientometric and qualitative analysis methods. The scientometric analysis includes co-authorship analysis, citation analysis, keyword analysis, and literature theme analysis. The results reveal that (1) there is a noticeable growth trend in research over the past two years, with a preference for using PLS-SEM over CB-SEM; (2) in terms of the number of published articles, leading contributors include Engineering Construction and Architectural Management, Sustainability, Journal of Construction Engineering and Management, and Buildings; (3) Ahmed Kineber is the most prolific researcher, having published the highest number of articles in this field, while Seulki Lee, Jungho Yu, Changwan Kim, Hyojoo Son, and Nicholas Chileshe have received the highest number of citations; (4) a visualization of keyword chronological analysis identifies and discusses the most frequently cited articles, providing insights into their significance and impact; (5) current research topics primarily focus on BIM, with most studies utilizing cross-sectional SEM and overlooking longitudinal SEM. The analysis results can assist scholars in familiarizing themselves with SEM-related literature and guide researchers in addressing existing issues for improvement.
A detailed qualitative discussion covers research design scopes, SEM technical intricacies, research gaps, and future research directions. In terms of research design, the current body of research focuses primarily on the application of BIM in developing regions. As a result, there is a noticeable lack of studies investigating other emerging technologies, more developed regions, and multiple regions. Furthermore, most studies in this field are cross-sectional. Longitudinal studies, on the other hand, cover a longer time span and may provide valuable insights into causal relationships. It is critical to recognize that the feasibility of both longitudinal and multi-regional studies is frequently dependent on the availability of relevant data. TAM, TOE, TPB, IDT, and UTAUT are examples of theoretical frameworks that originated in other domains and have been primarily used to discover technology adoption and usage in the construction industry.
In terms of the SEM technique, there is a growing trend favoring the use of PLS-SEM over CB-SEM, particularly in the last two years. Reflective measures continue to dominate SEM constructs, emphasizing the importance of researchers taking into account the relationships between LVs and their OVs in future research, especially since the increasingly popular PLS-SEM technique is better suited for formative measures. Notably, many articles lack the theoretical underpinning required for developing causal hypotheses at the structural model level and selecting appropriate indicators at the measurement model level. Furthermore, only a few articles delve into discussions concerning mediation and moderation effects, which are critical aspects of SEM that should be considered to make valuable contributions to the existing body of knowledge. Several research gaps and future directions are identified based on the review findings, including diversifying study areas and research topics, incorporating theoretical support for research design, and carefully selecting reflective or formative measures.
This review-based study combines an innovative scientometric analysis method with traditional qualitative discussion to provide a comprehensive overview of research investigating technology adoption and utilization in the construction industry via SEM. It adds to the existing body of knowledge by (1) using a science mapping approach to visualize and investigate the intricate relationships among journal sources, keywords, authors, and documents in the literature sample; (2) examining and discussing existing research by outlining research design issues and addressing the technical complexities and potential challenges associated with SEM techniques; and (3) identifying current research gaps and highlighting prospective avenues for future research, thereby providing valuable insights for both researchers and practitioners.
It should be noted that the current study has some limitations. First, the literature sample only includes English-language journal articles obtained from the Web of Science, Scopus, and Engineering Village databases. Second, the analysis of relevant literature on the adoption of SEM in the construction field is conducted solely based on the titles, author keywords, and abstracts without examining the core content. Finally, for VOSviewer, the outputs of the analysis may fluctuate slightly when using different processing thresholds. Future research would benefit from broadening the inclusion criteria to encompass other publication sources such as conference articles and books, as well as articles published in languages other than English, and exploring additional databases to provide additional valuable insights.

Author Contributions

Conceptualization, C.F., J.W., Z.Q. and M.S.; methodology, C.F., J.W., Z.Q., M.S. and J.Y.; validation, J.W., Z.Q., M.S., Z.S. and J.C.; formal analysis, C.F., Z.Q. and J.Y.; data curation, C.F., J.W., Z.Q. and J.Y.; writing—original draft preparation, C.F., J.W. and Z.Q.; writing—review and editing, M.S., J.Y., Z.S. and J.C.; visualization, J.W., Z.Q., M.S., J.Y., Z.S. and J.C.; supervision, C.F., J.W., M.S., Z.S. and J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shandong Province Natural Science Foundation, grant number ZR2023QG168.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

BIMBuilding information modeling
CAISComputerized accounting information system
CBCovariance-based
GBTSGreen building technology
GPRGround penetrating radar
ICIntelligent compaction
ICTInformation communication technologies
IDTInnovation diffusion theory
IoTInternet of things
LVsLatent variables
MICTMobile information and communication technology
OVsObserved variables
PLSPartial least squares
SEMStructural equation modeling
TAMTechnology acceptance model
TOETechnology–organization–environment
TPBTheory of planned behavior
UTAUTUnified theory of acceptance and use of technology
VR/ARVirtual reality or augmented reality
WBTMSWeb-based training or management systems

References

  1. Abioye, S.O.; Oyedele, L.O.; Akanbi, L.; Ajayi, A.; Delgado, J.M.D.; Bilal, M.; Akinade, O.O.; Ahmed, A. Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. J. Build. Eng. 2021, 44, 103299. [Google Scholar] [CrossRef]
  2. Chen, X.; Chang-Richards, A.Y.; Pelosi, A.; Jia, Y.; Shen, X.; Siddiqui, M.K.; Yang, N. Implementation of technologies in the construction industry: A systematic review. Eng. Constr. Archit. Manag. 2022, 29, 3181–3209. [Google Scholar] [CrossRef]
  3. Loosemore, M. Improving construction productivity: A subcontractor’s perspective. Eng. Constr. Archit. Manag. 2014, 21, 245–260. [Google Scholar] [CrossRef]
  4. Srivastava, A.; Jawaid, S.; Singh, R.; Gehlot, A.; Akram, S.V.; Priyadarshi, N.; Khan, B. Imperative role of technology intervention and implementation for automation in the construction industry. Adv. Civ. Eng. 2022, 2022, 6716987. [Google Scholar] [CrossRef]
  5. Zhu, W.; Zhang, Z.; Li, X.; Feng, W.; Li, J. Assessing the effects of technological progress on energy efficiency in the construction industry: A case of China. J. Clean. Prod. 2019, 238, 117908. [Google Scholar] [CrossRef]
  6. Okpala, I.; Nnaji, C.; Awolusi, I. Wearable sensing devices acceptance behavior in construction safety and health: Assessing existing models and developing a hybrid conceptual model. Constr. Innov. 2022, 22, 57–75. [Google Scholar] [CrossRef]
  7. Etemadi, R.; Hon, C.K.H.; Murphy, G.; Manley, K. The use of social media for work-related knowledge sharing by construction professionals. Archit. Eng. Des. Manag. 2020, 16, 426–440. [Google Scholar] [CrossRef]
  8. Poirier, E.A.; Staub-French, S.; Forgues, D. Measuring the impact of BIM on labor productivity in a small specialty contracting enterprise through action-research. Autom. Constr. 2015, 58, 74–84. [Google Scholar] [CrossRef]
  9. Ogunrinde, O.; Nnaji, C.; Amirkhanian, A. Quality management technologies in highway construction: Stakeholders’ perception of utility, benefits, and barriers. Pract. Period. Struct. Des. Constr. 2021, 26, 04020043. [Google Scholar] [CrossRef]
  10. Wang, W.; Zhang, S.; Su, Y.; Deng, X. Key factors to green building technologies adoption in developing countries: The perspective of Chinese designers. Sustainability 2018, 10, 4135. [Google Scholar] [CrossRef]
  11. Wu, P.; Zhao, X.; Baller, J.H.; Wang, X. Developing a conceptual framework to improve the implementation of 3D printing technology in the construction industry. Archit. Sci. Rev. 2018, 61, 133–142. [Google Scholar] [CrossRef]
  12. Kineber, A.F.; Oke, A.E.; Alyanbaawi, A.; Abubakar, A.S.; Hamed, M.M. Exploring the Cloud Computing Implementation Drivers for Sustainable Construction Projects—A Structural Equation Modeling Approach. Sustainability 2022, 14, 14789. [Google Scholar] [CrossRef]
  13. Hair, J.; Alamer, A. Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Res. Methods Appl. Linguist. 2022, 1, 100027. [Google Scholar] [CrossRef]
  14. Ahmed, Y.A.; Shehzad HM, F.; Khurshid, M.M.; Abbas Hassan, O.H.; Abdalla, S.A.; Alrefai, N. Examining the effect of interoperability factors on building information modelling (BIM) adoption in Malaysia. Constr. Innov. 2022, 24, 606–642. [Google Scholar] [CrossRef]
  15. AL-Hashmy, H.N.; Said, I.; Ismail, R. Analyzing the Impact of Computerized Accounting Information System on Iraqi Construction Companies’ Performance. Informatica 2022, 46. [Google Scholar] [CrossRef]
  16. Haenlein, M.; Kaplan, A.M. A beginner’s guide to partial least squares analysis. Underst. Stat. 2004, 3, 283–297. [Google Scholar] [CrossRef]
  17. Hair, J.F., Jr.; Hult GT, M.; Ringle, C.M.; Sarstedt, M.; Danks, N.P.; Ray, S. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook; Springer Nature: Cham, Switzerland, 2021; p. 197. [Google Scholar]
  18. Cole, D.A.; Preacher, K.J. Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychol. Methods 2014, 19, 300. [Google Scholar] [CrossRef]
  19. Hair, J.F., Jr.; Sarstedt, M. Factors versus composites: Guidelines for choosing the right structural equation modeling method. Proj. Manag. J. 2019, 50, 619–624. [Google Scholar] [CrossRef]
  20. Pişirir, E.; Uçar, E.; Chouseinoglou, O.; Sevgi, C. Structural equation modeling in cloud computing studies: A systematic literature review. Kybernetes 2020, 49, 982–1019. [Google Scholar] [CrossRef]
  21. Byrne, B.M. Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming; Routledge: Abingdon, UK, 2013. [Google Scholar]
  22. Waltman, L.; Van Eck, N.J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 2013, 86, 471. [Google Scholar] [CrossRef]
  23. Wang, W.; Gao, S.; Mi, L.; Xing, J.; Shang, K.; Qiao, Y.; Fu, Y.; Ni, G.; Xu, N. Exploring the adoption of BIM amidst the COVID-19 crisis in China. Build. Res. Inf. 2021, 49, 930–947. [Google Scholar] [CrossRef]
  24. Xiong, B.; Skitmore, M.; Xia, B. A critical review of structural equation modeling applications in construction research. Autom. Constr. 2015, 49, 59–70. [Google Scholar] [CrossRef]
  25. Zeng, N.; Liu, Y.; Gong, P.; Hertogh, M.; König, M. Do right PLS and do PLS right: A critical review of the application of PLS-SEM in construction management research. Front. Eng. Manag. 2021, 8, 356–369. [Google Scholar] [CrossRef]
  26. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  27. MacCallum, R.C.; Roznowski, M.; Necowitz, L.B. Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychol. Bull. 1992, 111, 490. [Google Scholar] [CrossRef]
  28. Hair, J.F., Jr.; Howard, M.C.; Nitzl, C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J. Bus. Res. 2020, 109, 101–110. [Google Scholar] [CrossRef]
  29. Maydeu-Olivares, A.; Shi, D.; Rosseel, Y. Assessing fit in structural equation models: A Monte-Carlo evaluation of RMSEA versus SRMR confidence intervals and tests of close fit. Struct. Equ. Model. A Multidiscip. J. 2018, 25, 389–402. [Google Scholar] [CrossRef]
  30. Bentler, P.M. Comparative fit indexes in structural models. Psychol. Bull. 1990, 107, 238. [Google Scholar] [CrossRef]
  31. Dash, G.; Paul, J. CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technol. Forecast. Soc. Chang. 2021, 173, 121092. [Google Scholar] [CrossRef]
  32. Statsenko, L.; Samaraweera, A.; Bakhshi, J.; Chileshe, N. Construction 4.0 technologies and applications: A systematic literature review of trends and potential areas for development. Constr. Innov. 2023, 23, 961–993. [Google Scholar] [CrossRef]
  33. Lu, K.; Gao, H.; Yu, H.; Liu, D.; Zhu, N.; Wan, K. Insight into variations of DOM fractions in different latitudinal rural black-odor waterbodies of eastern China using fluorescence spectroscopy coupled with structure equation model. Sci. Total Environ. 2022, 816, 151531. [Google Scholar] [CrossRef]
  34. Hire, S.; Sandbhor, S.; Ruikar, K. Bibliometric survey for adoption of building information modeling (BIM) in construction industry–a safety perspective. Arch. Comput. Methods Eng. 2022, 29, 679–693. [Google Scholar] [CrossRef]
  35. Saah AE, N.; Choi, J.H. Blockchain technology in the AEC industry: Scientometric analysis of research activities. J. Build. Eng. 2023, 72, 106609. [Google Scholar] [CrossRef]
  36. Eliwa, H.K.; Jelodar, M.B.; Poshdar, M.; Yi, W. Information and Communication Technology Applications in Construction Organizations: A Scientometric Review. J. Inf. Technol. Constr. 2023, 28, 286. [Google Scholar] [CrossRef]
  37. Nnaji, C.; Okpala, I.; Awolusi, I.; Gambatese, J. A systematic review of technology acceptance models and theories in construction research. J. Inf. Technol. Constr. (ITcon) 2023, 28, 39–69. [Google Scholar] [CrossRef]
  38. Ejidike, C.C.; Mewomo, M.C. Benefits of adopting smart building technologies in building construction of developing countries: Review of literature. SN Appl. Sci. 2023, 5, 52. [Google Scholar] [CrossRef]
  39. Felizardo, K.R.; Salleh, N.; Martins, R.M.; Mendes, E.; MacDonell, S.G.; Maldonado, J.C. Using visual text mining to support the study selection activity in systematic literature reviews. In Proceedings of the 2011 International Symposium on Empirical Software Engineering and Measurement, Banff, AB, Canada, 22–23 September 2011; IEEE: New York, NY, USA, 2011; pp. 77–86. [Google Scholar]
  40. Keim, D.A. Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 2002, 8, 1–8. [Google Scholar] [CrossRef]
  41. Kipper, L.M.; Furstenau, L.B.; Hoppe, D.; Frozza, R.; Iepsen, S. Scopus scientific mapping production in industry 4.0 (2011–2018): A bibliometric analysis. Int. J. Prod. Res. 2020, 58, 1605–1627. [Google Scholar] [CrossRef]
  42. Chen, C. Science mapping: A systematic review of the literature. J. Data Inf. Sci. 2017, 2, 1–40. [Google Scholar] [CrossRef]
  43. Wang, J.; Chen, J.; Hu, Y. A science mapping approach based review of model predictive control for smart building operation management. J. Civ. Eng. Manag. 2022, 28, 661–679. [Google Scholar] [CrossRef]
  44. Jin, R.; Zou, P.X.; Piroozfar, P.; Wood, H.; Yang, Y.; Yan, L.; Han, Y. A science mapping approach based review of construction safety research. Saf. Sci. 2019, 113, 285–297. [Google Scholar] [CrossRef]
  45. Sepasgozar, S.; Karimi, R.; Farahzadi, L.; Moezzi, F.; Shirowzhan, S.; Ebrahimzadeh, S.M.; Hui, F.; Aye, L. A systematic content review of artificial intelligence and the internet of things applications in smart home. Appl. Sci. 2020, 10, 3074. [Google Scholar] [CrossRef]
  46. Kim, H.; Choi, H.; Kang, H.; An, J.; Yeom, S.; Hong, T. A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities. Renew. Sustain. Energy Rev. 2021, 140, 110755. [Google Scholar] [CrossRef]
  47. Wang, J.; Li, M.; Skitmore, M.; Chen, J. Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends. Sustainability 2024, 16, 2290. [Google Scholar] [CrossRef]
  48. Van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef]
  49. Waltman, L.; Van Eck, N.J.; Noyons, E.C. A unified approach to mapping and clustering of bibliometric networks. J. Informetr. 2010, 4, 629–635. [Google Scholar] [CrossRef]
  50. Van Eck, N.J.; Waltman, L. Visualizing bibliometric networks. In Measuring Scholarly Impact; Springer: Cham, Switzerland, 2014; pp. 285–320. [Google Scholar]
  51. Zhou, K.; Wang, J.; Ashuri, B.; Chen, J. Discovering the Research Topics on Construction Safety and Health Using Semi-Supervised Topic Modeling. Buildings 2023, 13, 1169. [Google Scholar] [CrossRef]
  52. Lee, S.; Yu, J.; Jeong, D. BIM acceptance model in construction organizations. J. Manag. Eng. 2015, 31, 04014048. [Google Scholar] [CrossRef]
  53. Son, H.; Lee, S.; Kim, C. What drives the adoption of building information modeling in design organizations? An empirical investigation of the antecedents affecting architects’ behavioral intentions. Autom. Constr. 2015, 49, 92–99. [Google Scholar] [CrossRef]
  54. Ding, Z.; Zuo, J.; Wu, J.; Wang, J.Y. Key factors for the BIM adoption by architects: A China study. Eng. Constr. Archit. Manag. 2015, 22, 732–748. [Google Scholar] [CrossRef]
  55. Hosseini, M.; Banihashemi, S.; Chileshe, N.; Namzadi, M.O.; Udaeja, C.; Rameezdeen, R.; McCuen, T. BIM adoption within Australian Small and Medium-sized Enterprises (SMEs): An innovation diffusion model. Constr. Econ. Build. 2016, 16, 71–86. [Google Scholar] [CrossRef]
  56. Hosseini, M.R.; Banihashemi, S.; Rameezdeen, R.; Golizadeh, H.; Arashpour, M.; Ma, L. Sustainability by Information and Communication Technology: A paradigm shift for construction projects in Iran. J. Clean. Prod. 2017, 168, 1–13. [Google Scholar] [CrossRef]
  57. Zhao, X.; Feng, Y.; Pienaar, J.; O’Brien, D. Modelling paths of risks associated with BIM implementation in architectural, engineering and construction projects. Archit. Sci. Rev. 2017, 60, 472–482. [Google Scholar] [CrossRef]
  58. Alizadehsalehi, S.; Yitmen, İ. Modeling and analysis of the impact of BIM-based field data capturing technologies on automated construction progress monitoring. Int. J. Civ. Eng. 2018, 16, 1669–1685. [Google Scholar] [CrossRef]
  59. Zhao, X.; Wu, P.; Wang, X. Risk paths in BIM adoption: Empirical study of China. Eng. Constr. Archit. Manag. 2018, 25, 1170–1187. [Google Scholar] [CrossRef]
  60. Hong, Y.; Hammad, A.W.; Sepasgozar, S.; Akbarnezhad, A. BIM adoption model for small and medium construction organisations in Australia. Eng. Constr. Archit. Manag. 2019, 26, 154–183. [Google Scholar] [CrossRef]
  61. Yuan, H.; Yang, Y.; Xue, X. Promoting owners’ BIM adoption behaviors to achieve sustainable project management. Sustainability 2019, 11, 3905. [Google Scholar] [CrossRef]
  62. Olanrewaju, O.I.; Kineber, A.F.; Chileshe, N.; Edwards, D.J. Modelling the impact of building information modelling (BIM) implementation drivers and awareness on project lifecycle. Sustainability 2021, 13, 8887. [Google Scholar] [CrossRef]
  63. Olanrewaju, O.I.; Kineber, A.F.; Chileshe, N.; Edwards, D.J. Modelling the relationship between Building Information Modelling (BIM) implementation barriers, usage and awareness on building project lifecycle. Build. Environ. 2022, 207, 108556. [Google Scholar] [CrossRef]
  64. Aibinu, A.A.; Al-Lawati, A.M. Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Autom. Constr. 2010, 19, 714–724. [Google Scholar] [CrossRef]
  65. Darko, A.; Chan AP, C.; Yang, Y.; Shan, M.; He, B.J.; Gou, Z. Influences of barriers, drivers, and promotion strategies on green building technologies adoption in developing countries: The Ghanaian case. J. Clean. Prod. 2018, 200, 687–703. [Google Scholar] [CrossRef]
  66. Park, Y.; Son, H.; Kim, C. Investigating the determinants of construction professionals’ acceptance of web-based training: An extension of the technology acceptance model. Autom. Constr. 2012, 22, 377–386. [Google Scholar] [CrossRef]
  67. Yang, L.R.; Chen, J.H.; Wang, H.W. Assessing impacts of information technology on project success through knowledge management practice. Autom. Constr. 2012, 22, 182–191. [Google Scholar] [CrossRef]
  68. Lee, S.K.; Yu, J.H. Success model of project management information system in construction. Autom. Constr. 2012, 25, 82–93. [Google Scholar] [CrossRef]
  69. Alizadehsalehi, S.; Yitmen, I. Digital twin-based progress monitoring management model through reality capture to extended reality technologies (DRX). Smart Sustain. Built Environ. 2023, 12, 200–236. [Google Scholar] [CrossRef]
  70. Wong TK, M.; Man, S.S.; Chan AH, S. Exploring the acceptance of PPE by construction workers: An extension of the technology acceptance model with safety management practices and safety consciousness. Saf. Sci. 2021, 139, 105239. [Google Scholar] [CrossRef]
  71. Pan, M.; Pan, W. Understanding the determinants of construction robot adoption: Perspective of building contractors. J. Constr. Eng. Manag. 2020, 146, 04020040. [Google Scholar] [CrossRef]
  72. Huang, Y.; Trinh, M.T.; Le, T. Critical factors affecting intention of use of augmented hearing protection technology in construction. J. Constr. Eng. Manag. 2021, 147, 04021088. [Google Scholar] [CrossRef]
  73. Zhang, L.; Chu, Z.; He, Q.; Zhai, P. Investigating the constraints to building information modeling (BIM) applications for sustainable building projects: A case of China. Sustainability 2019, 11, 1896. [Google Scholar] [CrossRef]
  74. Zhang, L.; Chu, Z.; Song, H. Understanding the relation between BIM application behavior and sustainable construction: A case study in China. Sustainability 2019, 12, 306. [Google Scholar] [CrossRef]
  75. Mirpanahi, M.V.; Noorzai, E. Modeling the relationship between critical BIM attributes and environmental sustainability criteria using PLS-SEM technique. J. Archit. Eng. 2021, 27, 04021037. [Google Scholar] [CrossRef]
  76. Famakin, I.O.; Othman, I.; Kineber, A.F.; Oke, A.E.; Olanrewaju, O.I.; Hamed, M.M.; Olayemi, T.M. Building Information Modeling Execution Drivers for Sustainable Building Developments. Sustainability 2023, 15, 3445. [Google Scholar] [CrossRef]
  77. Murti, C.K.; Muslim, F. Relationship between Functions, Drivers, Barriers, and Strategies of Building Information Modelling (BIM) and Sustainable Construction Criteria: Indonesia Construction Industry. Sustainability 2023, 15, 5526. [Google Scholar] [CrossRef]
  78. Van Tam, N.; Quoc Toan, N.; Phong, V.V.; Durdyev, S. Impact of BIM-related factors affecting construction project performance. Int. J. Build. Pathol. Adapt. 2023, 41, 454–475. [Google Scholar] [CrossRef]
  79. Zhang, H.M.; Chong, H.Y.; Zeng, Y.; Zhang, W. The effective mediating role of stakeholder management in the relationship between BIM implementation and project performance. Eng. Constr. Archit. Manag. 2023, 30, 2503–2522. [Google Scholar] [CrossRef]
  80. Qiao, S.; Wang, Q.; Guo, Z.; Guo, J. Collaborative innovation activities and BIM application on innovation capability in construction supply chain: Mediating role of explicit and tacit knowledge sharing. J. Constr. Eng. Manag. 2021, 147, 04021168. [Google Scholar] [CrossRef]
  81. Shi, Q.; Wang, Q.; Guo, Z. Knowledge sharing in the construction supply chain: Collaborative innovation activities and BIM application on innovation performance. Eng. Constr. Archit. Manag. 2022, 29, 3439–3459. [Google Scholar] [CrossRef]
  82. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford publications: New York, NY, USA, 2023. [Google Scholar]
  83. Little, T.D. Longitudinal Structural Equation Modeling; Guilford Press: New York, NY, USA, 2013. [Google Scholar]
  84. Davis, F.D. A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Doctoral Dissertation, Massachusetts Institute of Technology, Cambridge, MA, USA, 1985. [Google Scholar]
  85. Tornatzky, L.G.; Fleischer, M. The Processes of Technological Innovation; Lexington Books: Lexington, MA, USA, 1990. [Google Scholar]
  86. Ajzen, I. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behavior; Springer: Berlin/Heidelberg, Germany, 1985; pp. 11–39. [Google Scholar]
  87. Rogers, E.M. Diffusion of Innovations, 5th ed.; Free Press: New York, NY, USA, 2003. [Google Scholar]
  88. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
  89. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  90. Baker, J. The technology–organization–environment framework. In Information Systems Theory: Explaining and Predicting Our Digital Society; Springer: New York, NY, USA, 2012; Volume 1, pp. 231–245. [Google Scholar]
  91. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  92. Moore, G.C.; Benbasat, I. Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 1991, 2, 192–222. [Google Scholar] [CrossRef]
  93. Bollen, K.A.; Bauer, D.J.; Christ, S.L.; Edwards, M.C. Overview of structural equation models and recent extensions. In Statistics in the Social Sciences: Current Methodological Developments; Wiley: Hoboken, NJ, USA, 2010; pp. 37–79. [Google Scholar]
  94. Kline, R.B.; Santor, D.A. Principles & practice of structural equation modelling. Can. Psychol. 1999, 40, 381. [Google Scholar]
  95. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use how to report the results of, P.L.S.-S.E.M. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  96. Arbuckle, J.L. Computer announcement amos: Analysis of moment structures. Psychometrika 1994, 59, 135–137. [Google Scholar] [CrossRef]
  97. Jöreskog, K.G. A general method for analysis of covariance structures. Biometrika 1970, 57, 239–251. [Google Scholar] [CrossRef]
  98. Ringle, C.M.; Wende, S.; Becker, J.M. SmartPLS 4. Oststeinbek: SmartPLS. Retrieved March 2022, 13, 2023. [Google Scholar]
  99. Jarvis, C.B.; MacKenzie, S.B.; Podsakoff, P.M. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res. 2003, 30, 199–218. [Google Scholar] [CrossRef]
  100. Peter, J.P. Construct validity: A review of basic issues and marketing practices. J. Mark. Res. 1981, 18, 133–145. [Google Scholar] [CrossRef]
  101. Diamantopoulos, A. Incorporating formative measures into covariance-based structural equation models. MIS Q. 2011, 35, 335–358. [Google Scholar] [CrossRef]
  102. Shipley, B. Cause Correlation in Biology: A User’s Guide to Path Analysis Structural Equations Causal Inference with R; Cambridge University Press: Cambridge, UK, 2016. [Google Scholar]
  103. Fan, Y.; Chen, J.; Shirkey, G.; John, R.; Wu, S.R.; Park, H.; Shao, C. Structural equation modeling (SEM) applications in ecological studies: An updated review. Ecol. Process. 2016, 5, 1–12. [Google Scholar] [CrossRef]
  104. Bentler, P.M.; Chou, C.P. Practical issues in structural modeling. Sociol. Methods Res. 1987, 16, 78–117. [Google Scholar] [CrossRef]
  105. Bollen, K.A. Latent variables in psychology and the social sciences. Annu. Rev. Psychol. 2002, 53, 605–634. [Google Scholar] [CrossRef] [PubMed]
  106. Duncan, T.E.; Duncan, S.C.; Strycker, L.A. An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application; Routledge: Abingdon, UK, 2013. [Google Scholar]
  107. Ringle, C.M.; Sarstedt, M.; Straub, D.W. Editor’s comments: A critical look at the use of PLS-SEM in “MIS Quarterly”. MIS Q. 2012, 36, iii–xiv. [Google Scholar] [CrossRef]
  108. Muller, D.; Judd, C.M.; Yzerbyt, V.Y. When moderation is mediated and mediation is moderated. J. Personal. Soc. Psychol. 2005, 89, 852. [Google Scholar] [CrossRef] [PubMed]
  109. Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 1986, 51, 1173. [Google Scholar] [CrossRef] [PubMed]
  110. Jia, J.; Zhang, M.; Yang, G. Factors influencing BIM integration with emerging technologies: Knowledge coupling perspective. J. Manag. Eng. 2022, 38, 04022001. [Google Scholar] [CrossRef]
  111. Yang, L.R.; Huang, C.F. Information platform to improve technological innovation capabilities: Role of cloud platform. J. Civ. Eng. Manag. 2016, 22, 936–943. [Google Scholar] [CrossRef]
  112. Jiang, S.; Ma, G.; Jia, J.; Wu, M.; Wu, Z. Mobile ICT overuse in the construction industry: Effects on job burnout of project managers. J. Constr. Eng. Manag. 2022, 148, 04022024. [Google Scholar] [CrossRef]
Sustainability 16 03824 g001
Figure 1. Workflow of the research methods.
Figure 1. Workflow of the research methods.
Sustainability 16 03824 g001
Sustainability 16 03824 g002
Figure 2. Visualization of journals.
Figure 2. Visualization of journals.
Sustainability 16 03824 g002
Sustainability 16 03824 g003
Figure 3. Visualization of researchers.
Figure 3. Visualization of researchers.
Sustainability 16 03824 g003
Sustainability 16 03824 g004
Figure 4. Visualization of co-authorship network for geographical regions.
Figure 4. Visualization of co-authorship network for geographical regions.
Sustainability 16 03824 g004
Sustainability 16 03824 g005
Figure 5. Visualization of keywords.
Figure 5. Visualization of keywords.
Sustainability 16 03824 g005
Sustainability 16 03824 g006
Figure 6. Visualization of documents.
Figure 6. Visualization of documents.
Sustainability 16 03824 g006
Sustainability 16 03824 g007
Figure 7. Timeline-based choice between CB-SEM and PLS-SEM.
Figure 7. Timeline-based choice between CB-SEM and PLS-SEM.
Sustainability 16 03824 g007
Sustainability 16 03824 g008
Figure 8. Reflective (on the left) and formative (on the right) measures illustration.
Figure 8. Reflective (on the left) and formative (on the right) measures illustration.
Sustainability 16 03824 g008
Table 1. Quantitative metrics of most productive journals.
Table 1. Quantitative metrics of most productive journals.
JournalsNumber of PublicationsTotal CitationsAverage Publication YearAverage CitationsNormalized CitationsAverage Normalized Citations
Engineering Construction and Architectural Management18312202117.3316.900.94
Sustainability18194202110.7815.870.88
Journal of Construction Engineering and Management1211220219.338.140.68
Buildings112420222.184.550.41
Applied Science64220217.006.621.10
Automation in Construction6511201185.177.631.27
Journal of Management in Engineering6187202131.178.481.41
Construction Innovation54120208.202.380.48
Journal of Civil Engineering and Management550201910.002.130.43
Architectural Engineering and Design Management32620218.672.170.72
Journal of Facilities Management32520228.335.911.97
Table 2. Quantitative metrics of the highly cited researchers.
Table 2. Quantitative metrics of the highly cited researchers.
Researcher NameNumber of PublicationsTotal CitationsAverage Publication YearAverage CitationsNormalized CitationsAverage Normalized Citations
Lee, Seulki4240201760.003.540.89
Yu, Jungho4240201760.003.540.89
Kim, Changwan22312013115.502.921.46
Son, Hyojoo22312013115.502.921.46
Chileshe, Nicholas3194201964.6716.595.53
Kineber, Ahmed15164202210.9330.102.01
Jeong, David11542015154.002.032.03
Aibinu, Ajibade A.11452010145.002.002.00
Al-Lawati, Ahmed Murtadha11452010145.002.002.00
Lee, Sungwook11242016124.001.631.63
Table 3. Twenty most-referenced articles in the literature sample.
Table 3. Twenty most-referenced articles in the literature sample.
ArticleTitleTotal CitationsNormalized Citations
Lee et al. [52]BIM Acceptance Model in Construction Organizations1542.03
Aibinu et al. [64]Using the PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding1452.00
Son et al. [53]What drives the adoption of building information modeling in design organizations? An empirical investigation of the antecedents affecting architects’ behavioral intentions1241.63
Darko et al. [65]Influence of barriers, drivers, and promotion strategies on green building technologies adoption in developing countries: The Ghanaian case1113.56
Park et al. [66]Investigating the determinants of construction professionals’ acceptance of web-based training: An extension of the technology acceptance model1071.28
Ding et al. [54]Key factors for the BIM adoption by architects: a China study1021.34
Hosseini et al. [55]BIM adoption within Australian Small and Medium-sized Enterprises (SMEs): an innovation diffusion model962.77
Hong et al. [60]BIM adoption model for small and medium construction organizations in Australia803.02
Yang et al. [67]Assessing impacts of information technology on project success through knowledge management practice720.86
Lee et al. [68]Success model of project management information system in construction710.85
Olanrewaju et al. [63]Modelling the relationship between Building Information Modeling (BIM) implementation barriers, usage, and awareness on building project lifecycle5510.76
Alizadehsalehi and Yitmen [69]Digital twin-based progress monitoring management model through reality capture to extended reality technologies (DRX)5315.09
Zhao et al. [59]Risk paths in BIM adoption: empirical study of China481.54
Olanrewaju et al. [62]Modelling the Impact of Building Information Modelling (BIM) Implementation Drivers and Awareness on Project Lifecycle433.06
AlizadeSalehi et al. [58]Modelling and analysis of the impact of BIM-based field data capturing technologies on automated construction progress monitoring391.25
Yuan et al. [61]Promoting Owners’ BIM Adoption Behaviors to Achieve Sustainable Project Management351.32
Zhao et al. [57]Modelling paths of risks associated with BIM implementation in architectural, engineering and construction projects351.75
Wong et al. [70]Exploring the acceptance of PPE by construction workers: An extension of the technology acceptance model with safety management practices and safety consciousness302.13
Hosseini et al. [56]Sustainability by Information and Communication Technology: A paradigm shift for construction projects in Iran262.77
Pan and Pan. [71]Understanding the Determinants of Construction Robot Adoption: Perspective of Building Contractors251.67
Table 4. Frequently used theories and their key constructs with descriptions.
Table 4. Frequently used theories and their key constructs with descriptions.
TheoryKey ConstructsDescription
TAM1. Perceived usefulness
2. Perceived ease of use		1. “The degree to which a person believes that using a particular system would enhance their job performance” [89]
2. “The degree to which a person believes that using a particular system would be free of effort” [89]
TOE1. Technological context
2. Organizational context
3. Environmental context		1. “All of the technologies (both already in use and available in the marketplace) that are relevant to the firm” [90]
2. “Characteristics and resources of the firm, including linking structures between employees, intra-firm communication processes, firm size, and the amount of slack resources” [90]
3. “The structure of the industry, the presence or absence of technology service providers, and the regulatory environment” [90]
TPB1. Attitude toward behavior
2. Subjective norm
3. Perceived behavioral control		1. “The degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question” [91]
2. “The perceived social pressure to perform or not to perform the behavior” [91]
3. “The perceived ease or difficulty of performing the behavior” [91]
IDT1. Relative advantage
2. Compatibility
3. Complexity
4. Trialability
5. Observability		1. “The degree to which an innovation is perceived as being better than the idea it supersedes” [92]
2. “The degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential” [92]
3. “The degree to which an innovation is perceived as relatively difficult to understand and use” [92]
4. “The degree to which an innovation may be experimented with on a limited basis” [92]
5. “The degree to which the results of an innovation are visible to others” [92]
UTAUT1. Effort expectancy
2. Performance expectancy
3. Social influence
4. Facilitating conditions		1. “The degree of ease associated with the use of the system” [88]
2. “The degree to which an individual believes that using the system will help them to attain gains in job performance” [88]
3. “The degree to which an individual perceives that important-others believe they should use the new system.” [88]
4. “The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system.” [88]
Table 5. Differences between reflective and formative measures.
Table 5. Differences between reflective and formative measures.
KEY ConstructsDescription
LV-OV relationshipOVs are manifested by the LVOVs jointly define the LV
Causality directionFrom LV to OVsFrom OVs to LV
InterchangeabilityOVs are interchangeableOVs need not be interchangeable
IntercorrelationOVs are expected to be intercorrelatedOVs can have any pattern of intercorrelation
MulticollinearityMulticollinearity is a virtueMulticollinearity should be ruled out
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

Fu, C.; Wang, J.; Qu, Z.; Skitmore, M.; Yi, J.; Sun, Z.; Chen, J. Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review. Sustainability 2024, 16, 3824. https://doi.org/10.3390/su16093824

AMA Style

Fu C, Wang J, Qu Z, Skitmore M, Yi J, Sun Z, Chen J. Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review. Sustainability. 2024; 16(9):3824. https://doi.org/10.3390/su16093824

Chicago/Turabian Style

Fu, Chuyou, Jun Wang, Ziyi Qu, Martin Skitmore, Jiaxin Yi, Zhengjie Sun, and Jianli Chen. 2024. "Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review" Sustainability 16, no. 9: 3824. https://doi.org/10.3390/su16093824

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