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Article

A Mathematical Analysis of 4IR Innovation Barriers in Developmental Social Work—A Structural Equation Modeling Approach

by
Paramjit Singh Jamir Singh
1,*,
Ayodeji Emmanuel Oke
1,2,3,*,
Ahmed Farouk Kineber
4,5,*,
Oludolapo Ibrahim Olanrewaju
6,
Olayinka Omole
7,
Mohamad Shaharudin Samsurijan
1 and
Rosfaraliza Azura Ramli
8,*
1
School of Social Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
2
Department of Quantity Surveying, Federal University of Technology Akure, Akure 340110, Nigeria
3
CIDB Centre of Excellence, Faculty of Engineering and Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
4
Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
5
Department of Civil Engineering, Canadian International College (CIC), Zayed Campus, 6th October City 12577, Giza, Egypt
6
Wellington School of Architecture, Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand
7
Irene Construction Science Division, Christopher Gibbs College of Architecture, University of Oklahoma, Norman, OK 73019, USA
8
College of Law, Government and International Studies, School of Government, Universiti Utara Malaysia, Changlun 06050, Malaysia
*
Authors to whom correspondence should be addressed.
Mathematics 2023, 11(4), 1003; https://doi.org/10.3390/math11041003
Submission received: 25 January 2023 / Revised: 8 February 2023 / Accepted: 10 February 2023 / Published: 16 February 2023
(This article belongs to the Special Issue Mathematical Theories and Models in Environmental Science)
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Abstract

:
The fourth industrial revolution (4IR) era also known as digital age is central to the advancement of the construction industry as the industry is currently facing a myriad of challenges, including poor productivity and project failure. Therefore, there is an urgent need for industry to adopt 4IR innovations to increase the building business’s performance. The study explored the relationship between the critical barriers to 4IR innovations to foster sustainable development. The study embraced a numerical exploration approach which employed a questionnaire to obtain information from building industry experts. The questionnaire data were used to classify the 4IR barriers into policy and structure, readiness, and acquisition, using Exploratory Factor Analysis (EFA). Likewise, a predictive model was developed using Structural Equation Modelling-Partial Least Square (SEM-PLS). It explained the relationship between the barrier categories and the barriers to 4IR innovation adoption for sustainable development. The results showed that policy and structure were critical components of 4IR adoption that the stakeholders of the construction industry must pay close attention to. The study also provided valuable areas for future research to enhance 4IR innovation adoption for sustainable development.

1. Introduction

Recently, the globe experienced many technological advancements, such as introducing of steam machines and hydraulics to the production process in factories in the United Kingdom (UK), tagging the initial groundswell of the industrial revolution. Further, people employ the codes of labor division to manufacture to improve mass production efficiency [1]. The division of labor has resulted in the separation of production components. It resulted in the manufacturing of affordable products. This era was identified as the second industrial revolution. As humans continuously conceive ideas, many innovations were intended. The incursion of information and communication technology and electronics was seen during the third industrial revolution, with extensive automation application in the manufacturing process [2,3]. Currently, the world is at the point of the fourth industrial revolution (4IR). However, the 4IR has been the basis for many divergences concerning how different industries function and operate. The 4IR is described as a revolutionary and quick change globally characterized by technology fusion, clouding the outlines among the biological, digital and physical spheres is rapidly changing the pattern of organizational functions and how these organizations offer services [4].
The 4IR has been extensively considered and classified by rapid and disruptive digitization, and changes are a novel field worldwide. The 4IR utilizes cyber-physical systems (CPS) in the production sector, which is the fundamental feature disparity between the first, second and third industrial revelations [2,3]. The concept of 4IR is primarily perceived as CPSs [5,6]. The profound assimilation of intelligent and networking systems establishes the underlying context of the 4IR [7]. Thus, it signifies that humans rely on the advantages of the information and communication technology (ICT) forwarded by General Electric [8,9]. It embraces a comparable technological conception to CPSs. These enable technologies that develop the integration of reality and virtuality [10], a network where intelligent objects communicate with each other.
However, the 4IR is not limited to the manufacturing or engineering process; it is expressed in different relevant fields, such as business [11,12] and other fields of human endeavor. Moreover, the building industry is not an exception to its application and usage [13]. Building is an extensive horizontal industry, supporting all industries where value realization virtually occurs in assets or facilities development, i.e., structure. Building personnel assigns only about 30% of their operational time to their primary duty while reorganizing the construction location, consecutive errands, observing equipment and resources, and assembling occupied the remaining time, i.e., 70% [14]. Consequently, it affects the performance of building projects. However, construction projects’ performance is determined by whether the tasks have been carried out on time and met the client satisfaction, methodological specification, and budget requirements [15,16,17,18]. Additionally, some studies have examined aspects such as aesthetics, quality, and safety as other project performance measures. Development of building projects comprises many parties, different processes, diverse stages and phases of activity and high input from both the private and public sectors.
Construction project development involves numerous parties, various processes, different phases and stages of work and a great deal of input from both the public and private sectors, aiming to bring successful project delivery. The success level in delivering the building projects and developing activities will heavily rely on the quality of the organizational, technical, financial and managerial accomplishment of individual parties, at the same time considering the accompanying risk management, political and economic stability, and business atmosphere. The building industry in Africa is yet to embrace the new 4IR trends fully and is still struggling to improve the management of many tasks employees are burdened with to facilitate the project implementation and realize the project performance [19]. Therefore, this research explores the barriers to 4IR principles adoption by providing sufficient information concerning improved project performance using these tools.

2. Literature Review

Each industrial revolution is associated with its challenges and benefits concerning the nations’ socioeconomic status that have embraced the such revolution. For example, Great Britain steered the first revolution by inventing the saleable steam engine. It has revolutionized transportation and communication and triggered many other industrial developments. The United States pioneered the second industrial revolution through telephone communication revolutionization. The internet was the primary factor and succeeded in the third industrial revolution. It is considered a civic infrastructure technology rather than a copyrighted technology [20]. The Internet has transformed the global economic landscape. The transformation is projected to stay with the Internet of things (IoT). Rifkin [21], authenticates this development in his theory of zero marginal cost, which stresses connectivity in his expectation for a cooperative economy that will substitute the capital system in its current practice- with IoT as the primary driver.
The four industrial revolutions have produced increased economic growth and productivity and improved the standard of living for the nations that succeeded in reaping most of their positive impacts, such as high-quality services and goods. Conversely, income distribution in the advanced nations resulting from the revolution was unrealistic. Unquestionably not at the world turn where disparity has become a significant challenge with climate change and other sustainability concerns. The fast depletion of natural resources and its consequences on upcoming generations and the environment has created an unprecedented global ecological challenge. Consequently, social innovation and sustainability concepts have drawn international attention as prospective resolutions. The world initiative by the United Nations (UN) concerning sustainable development goals (SDGs has directed a significant implication obligating comprehensive economic and social development [22]. Ground-breaking hard work, such as the sustainable livelihood technique, is required to join environmental and socioeconomic concerns [23]. The following barriers have been identified as obstacles to the 4IR tools.

2.1. A Resistance to Change

The current business issues are not related to the dearth of disruptive concepts; there are many. The concern concerns ‘when and if someone might try disruptive modernization within a customary business [24]. Thus, the corporate/organizational resistant system will respond and perhaps confront the disruptors and innovators. However, before proceeding with the concept of the ‘immune system’, it is noteworthy to explain it. “An organization’s immune system, like the human immune system, protects against change (intrusions) by erecting a powerful barrier. The organizational immune system comprises the people, policies, procedures, processes, and culture it creates to prevent change, regardless of the consequences [25]. How an organization introduces innovation tends to magnify the immune system problem. Organizations often ask external consultants what needs to be done to achieve transformation. Otherwise, they invest in or buy external start-ups. Either way, the organization’s immune system might attack, usually because the corporate immune system reacts to whatever it considers ‘foreign DNA’. Transforming an organization is not only about the organization itself but also about updating the mindset and knowledge base of the people who work for it. In addition, the construction industry has been generally adjudged to be rigid and slow to changing climes, patterns and modus operandi [19].

2.2. Knowledge and Competency in Computing

The fourth industrial revolution (4IR) is typified by Cyber-Physical Systems (CPSs), and these systems are controlled and monitored by higher programming languages and adequate computing [26]. Instead, excellent computing knowledge is needed for anyone or any organization using these technologies. The problem, however, is that not many construction professionals have this computing knowledge [27]. Until recently, university students enrolled in the Built Environment courses did not have anything relating to computing in their curriculum [28]. This is a significant barrier to embracing 4IR innovations. The construction industry will need principal input from individuals not in the construction field to fully grasp the usage of these technologies.

2.3. Difficulty in Understanding New 4IR Technologies

A focus on digital technologies better summarizes the range of tools now in use by young people than would the use of a term such as ICT [29]. Digital technologies, such as computers, mobile phones, and players of downloadable audio or games consoles, may be hardware-based. However, it may be software-based, as with web applications, social networking spaces, computer games or chat sites. In the case of this paper, the term also encompasses technologies such as virtual reality, Integrated Learning Systems and multimedia since awareness concerning 4IR technologies developed, consequently, the reluctance to accept and learn the usage of these technologies [30].

2.4. Leadership

Leadership is very crucial to the success of any project, construction, or non-construction. Good Leadership ensures that things are put in place that will enhance the chances of success, including 4IR technologies. Therefore, this dramatic increase in the development of technology and its impact on life in its broadest terms cannot be negated. Hence, it puts Leadership at the center of the paradox of beneficial transformations and profound challenges. However, it is essential to understand: How should leaders position themselves for a future of exponential automation across the various sectors of the economy?
Moreover, how should leadership practice evolve to steer the anticipated disruptions to organizations and associated impact on the social fabric? Consequently, Chui et al. [31] posited that the organizational and Leadership implications are profound and that leaders to front-line managers must redefine jobs and processes to ensure organizational longevity. These barriers should be addressed in the construction industry for proper implementation and adoption of 4IR innovations for construction practice.

2.5. Strategy and Investment

Regardless of the field, sector, profession or specialty, one of the ever-present barriers to the successful adoption and implementation of 4IR Innovations is ‘low investment’. Ntombela et al. [32] argued that there had been very little or insignificant investments in research into 4IR as regards the agricultural industry. The effect is that the farmers in Southern Africa have not begun reaping the countless benefits of using 4IR innovations in the agricultural industry. This can also be related to the construction industry. Very little has gone by investment into making our practice more competitive and concurrent using these disruptive 4IR technologies.

2.6. Quality of Data and Information

Another significant barrier that can deter the process and benefits of using 4IR innovation is the ‘quality of data and information’ [33]. 4IR innovations from BIM to AI need accurate data to function at maximum capacity. These innovations will not be effortless to produce the best results if the quality of data being fed to them is not up-to-date or good enough. Construction professionals must take cognizance of this barrier and do well to avoid it [34]. Data quality, assurance, and control should be ensured for drawings, project scheduling, and specifications to achieve the desired results.

2.7. A Limited Number of Skilled Personnel

Like many other industrial sectors, the construction industry still faces limited skilled professionals who can competently operate 4IR Innovations [35]. Similar challenges are faced when there are new technologies or innovations. There is an immense concern regarding the prospect for the tech and digital sectors. The significant existing barrier at the moment relates to sourcing talents. It turns out to be a wholly blown predicament in the upcoming years. The impacts of predicament will have a massive impact since all other economic sectors are touched by technology [36,37,38]. This problem was further compounded by the need for trained tools and labor not limited to the tech industry since other sectors require specialized human resources. Consequently, if there are no skilled professionals to use these technologies and apply them for construction purposes, there will be difficulties in adopting and implementing 4IR innovations.

2.8. The Fragmented Nature of the Construction Industry

The industry-level breaking up comes about when the number of small and medium enterprises proliferates and is accompanied by a decline in the number of large industries. In such circumstances, companies typically lack big market share, cannot stimulate significant outcomes for the trade and are incapable of creating intra-company linkages [39]. Conversely, specialization can trigger simultaneous knowledge-sharing problems within and among organizations. Moreover, the established knowledge in particular contexts is somewhat ‘localized’, and much empirical knowledge established in exercise remains implicit. Therefore, it is hard to transmit [40]. Fragmentation happens as a consequence of characteristics of different industries and as a result of other reasons. This study focused only on the reasons connected to building activities. The building project is client-contingent, and the design is located on a particular site that needs additional construction [40].

2.9. High Capital and Setup Cost

It is commonly recognized that the price of setting up these technologies is relatively high, and the capital needed to start up is highly intensive. This is a significant barrier besetting the adoption and implementation of 4IR innovations [41]. Additionally, the economic effect of COVID-19 on African countries has rapidly made the exchange rate against the United States Dollar plummet. Thus, it is harder to transact for these technologies, mainly from Europe, Southern America and Asia. Table 1 summarizes the barriers to 4IR innovation adoption extracted from existing literature.

3. Model Development and Research Method

Based on the reviewed literature concerning the barriers to 4IR innovation adoption, 21 barriers were established and deemed appropriate. It is further illustrated in Figure 1. Subsequently, a questionnaire survey was conducted by sending a list of barriers to 4IR innovation adoption to construction industry experts with pertinent construction knowledge. It was conducted to verify the adequacy and clearness concerning 4IR innovation barriers which deter its adoption, in addition to analyzing these obstacles and their types using the exploratory factor analysis (EFA).

3.1. Model Development

The structural equation modelling-partial least square (SEM-PLS) has drawn attention from various disciplines, exceptionally social and business sciences [59]. Further research using the SEM-PLS technique has been conducted and published by popular SSCI Journals [60,61,62]. The newest version of SMART-PLS 3.2.7 edition was used to assess the data gathered and to model the priority concerning barriers to 4IR innovation implementation using SEM. The SEM-PLS was initially recognized for its robust predicting potentials upon covariance-based-structural equation modelling (CB-SEM) [63], even though the disparity among the dual approaches is relatively low [64]. The mathematical analysis conducted in this research consists of the analytical and structural model assessment procedure.

3.1.1. Common Method Variance

The common method bias (CMB) is a product of common method variance (CMV). The CMB aids in describing the error (or variance) in the analysis results, which is related to the analytical procedure as an alternative to the concepts symbolized by the methods [65]. It can be defined as the variance overlap attributed to concepts. Likewise, the CMV is complicated whenever data, such as self-gathered data using a questionnaire, is obtained from a particular source [66,67]. In some situations, the self-gathered data can exaggerate or avert the extent of the assessed relationships and consequently generate problems [67,68]. It can be critical, particularly for this research, since all the data sets are self-gathered, idiosyncratic and derived from a solitary source. Hence, it is essential to tackle these concerns to identify any possible CMV. A single factor proper single factor test was performed following Harman et al. [69] experiments [70]. A single factor is derived from facto analysis explaining the large part of the variance [67].

3.1.2. Analytical Model

The analytical model uncovers the existing relationship among variables and their underlying structure [71]. The subsequent section addressed the discriminant and convergent validities of the analytical model.

Convergent Validity

Convergent validity (CV) epitomizes the degree of concordance between binary or more variables (or barriers) of a similar concept or construct [72]. It is regarded as a subgroup of the construct’s validity. Concerning PLS, the CV of the computed constructs can be defined using three tests [73]. These are (i) composite reliability scores (Pc), Cronbach’s alpha and average variance estimated (AVE). Nunnally and Bernstein [74] recommended a threshold value of 0.70 (Pc) as reasonable composite reliability. Concerning any research type, values higher than 0.60 and 0.70 for investigative study are deemed acceptable [75]. To conclude, the AVE is the last test, and it is considered a standard computation performed to evaluate the CV of the model’s constructs. I have higher values above 0.50, indicating a good CV [75].

Discriminant Validity

The discriminant validity (DV) specifies that the studied issues are experientially distinctive and advocates that any dimensions do not identify the construct studied in SEM [76]. Campbell and Fiske [77] argued that correspondence between measures speckled from one another must not be too high if the DV is to be established.

Structural Model Analysis

This study aimed to show the significant barriers to 4IR adoption via the SEM. It can first be achieved by identifying the path coefficients. Consequently, a one-way causal relationship or path relation has been theorized between constructs of 4IR barriers (£) and 4IR adoption barriers (μ). Hence the operational connection among £, μ, and ∈ 1 principle within the structural model has been recognised as an inner correlation that can be depicted using a linear equation [78]:
μ = β £ + ∈ 1
where the path coefficient connecting 4IR concepts is β, the residual adjustment at the operational level is thought to reside in ∈ 1 . Thus, the β is the standardised regression weight, similar to the multiple regression model’s β weight. Its signs must accord with what is predicted by the model and be significant statistically. The difficulty lies with establishing the path coefficients’ significance (β). Concerning the CFA, a bootstrapping method found in the SmartPLS 3.2.7 software was applied to assess the path coefficient’s standard errors. This was performed using 5000 sub-samples built on the Henseler et al. [59]’s recommendation. Thus, it has defined the t-statistics for testing the hypothesis. Three functional equations for 4IR concepts were established for the PLS model. It represents the internal relations among the concepts and Equation (1).

4. Data Collection

During data collection, stakeholders of potential housing construction industry stakeholders were contacted in Nigeria using a questionnaire tool to assess the barriers to 4IR adoption. The survey tool was divided into three major components: (i) the respondents’ demographic profile, (ii) the 4IR adoption barriers, and (iii) open-ended questions, which were added to enable experts to add any relevant barriers that the stakeholders have identified as necessary. Three major groups were interviewed. These include (i) clients, (ii) consultants, and (iii) contractors. These are further subdivided based on occupation or profession: mechanical, electrical, and structural engineers, architects, and quantity surveyors. The study population measured the 4IR adoption barriers based on experience and information via a five-point Linkert scale (5—very high, 4—high, 3—average, 2—low and 1—very low). This measure has been extensively used in the existing literature [79,80,81,82,83,84]. The 4IR is relatively recent in Nigeria. Thus, a stratified sampling technique of a specific subpopulation has been measured [85].
Furthermore, the selection of this study’s sample size was built on the procedural purpose exploration [86,87]. Kline [88] posited that a multifaceted path model required at least 200 or more samples. In contrast, Yin [89] argued that at least cases above 100 are adequate for SEM. Since the SEM method was used in this study, 257 respondents were derived from 348 initially identified respondents. The 257 participants were contacted through self-administration of a questionnaire which accounted for a 73.85% response rate. The level of return was deemed adequate for this type of research [90,91].

5. Results

5.1. Exploratory Factor Analysis

The factorability structure of twenty-one items linked to 4IR barriers was measured using the exploratory factor analysis (EFA) method. Many recognised factorability constructs have been employed for correlation. For instance, Kaiser-Meyer-Olkin (KMO) is used to measure factor homogeneity to verify if the fractional correlations between variables are the least possible [92]. The sampling acceptability method concerning the amount of data set and the range of the study population has been presented in Table 2. According to Tabachnick et al. [93], Bartlett’s Test of Sphericity has to be weighty (p < 0.05) for acceptable factor analysis. They argued that the KMO index ranged from 0–1; the more the value of KMO is closer to 1, the more the suitability of factor analysis. Therefore, the KMO value > 0.60 is deemed more appropriate for factor analysis. This study revealed that the KMO index, 0.771, and the Sphericity Test were significant (0.000); hence, factor analysis is suitable in both cases.
The diagonals of an anti-image matrix of correlation are >0.50, indicating the cogency of the addition of each construct in the factor analysis. The preliminary commonalities are variance estimates of each construct which all factors have accounted for. Lesser values (<0.30) indicate construct(s) which do not fit appropriately with factor explanation. In this study, the primary commonalities are higher than the threshold. The entire factor loadings are higher than 0.50. Three factors were extracted from the EFA exploration involving 21 variables. These factors have eigenvalues >1. These three factors accounted for 51.477% of the total variance; it is essential to mention the three components extracted for concise and correct interpretation. Based on the existing literature, there is little principle for specifying factor analysis’s extracted components. Hence naming these factors is subjective and built on the background, perception, and education/training of the analyst/researcher/viewer. After carefully considering how naming should be done, some names sprung up as (i) Policy and Structure Barriers, (ii) Readiness Barriers, and (iii) Acquisition Barriers.
Policy and Structure Barriers, the first component extracted, contained thirteen (13) items loaded in it; it depicted barriers that have to do with the policy and structure already in place that influence or limit the adoption of 4IR innovations. The thirteen items loaded found in this component are as follows; the disjointed nature of the building business, with a significance of 0.738; limited availability of resources to the SMEs, with a significance of 0.698; knowledge and competency in computing, with a significance of 0.678; a limited number of skilled personnel, with a significance of 0.667; lack of protocols for coding objects, with a significance of 0.635; fear, with a significance of 0.623; cost of maintenance is very high, with a significance of 0.609; legal and contractual issues, with a significance of 0.560; high capital and setup cost, with a significance of 0.504; interoperability and compatibility, with a significance of 0.490; difficulty understanding that new technology, with a significance of 0.439; resistance to change with a significance of 0.405; and increased risk exposure, with a significance of 0.402.
The name propounded for the second extracted component is “Readiness Barriers”. Readiness barriers refer to all issues concerning the readiness of construction professionals to use these new technologies, be it know-how or funds or data. The items loaded under this component are five (5), and they include training and learning, with a significance of 0.833; strategy and investment, with a significance of 0.733; organizational structure, with a significance of 0.729; quality of data and information, with a significance of 0.723; and Leadership, with a significance of 0.440 (Table 3).
“Acquisition Barriers” comes last as the name accrued to the third extracted component. It typically signifies variables concerning processes and difficulties involved in acquiring the technologies and getting them to function appropriately. Three (3) items are strongly loaded under this component: difficulties of acquiring these technologies locally due to unavailability, with a significance of 0.702; time spent on setting-up, with a significance of 0.598; and government policies, with a significance of 0.578.
Measurements of reliability were established for factors derived through the EFA. Variables concerning each stage of factor analysis have been measured based on the variable’s highest loading within the matrix structure. Nunnally [94] recommended that the Cronbach alpha value be >0.60 for freshly started dimensions. However, if the average value is 0.70, those >0.75 are deemed exceedingly correct. Hence, the Cronbach alpha (values) results are likewise suitable since they are >0.60. The average correlations of the items data set are >0.3 for the entire variables. It indicated the variable’s internal reliability [95]. Consequently, a conceptual model was proposed following the factor analysis (Figure 2); it comprised three hypotheses as follows:
H1. 
Policy and structure positively influence 4IR innovation adoption barriers.
H2. 
Readiness positively influences 4IR innovation adoption barriers.
H3. 
Acquisition factor positively influences 4IR innovation adoption barriers.
Mathematics 11 01003 g002 550
Figure 2. Research conceptual framework.
Figure 2. Research conceptual framework.
Mathematics 11 01003 g002

5.2. SEM-PLS Model Analysis

5.2.1. Common Method Bias

The common method bias (CMB) is the measurement of variance (error), which influences the study’s validity. It represents a systematic variance error linked to the estimated and measured variables [96]. It can be calculated using Harman’s single factor models evaluation, which reveals various measurements structure [70]. This study applied a single-factor test (SFT) to calculate the standard method’s variance [97]. Suppose the total variance of factor(s) is below 50%; the data has not been influenced by CMB [70]. The results revealed that the initial factor(s) set accounts for 23% of the total variance. It further shows that the results are affected by common method variance (CMV) because it is below 50% [70,98].

5.2.2. Analytical Model

The reflective measurement model evaluation (or barriers) in SEM-PLS obliges the internal consistency assessment, convergent and discriminant validity. As soon as the validity and reliability of the analytical model have been established, the structural model can be assessed [99]. Results revealed that the entire model’s constructs had met the α and P c threshold of >0.70 and hence are deemed suitable [100], as indicated in Table 4.
In addition, results in Table 4 revealed that the entire model’s constructs had passed the AVE test. The acceptable threshold for the AVE test has to be above 0.50 [73]. The AVE’s estimated values using PLS algorithm 3.0, have been summarized in Table 5. It further indicated that the entire model’s constructs in this analysis are above 50%. These results revealed that the analytical model is internally consistent and convergent. It also showed that the analytical variables are accurately measured for individual construct(s) or group and measures no other construct(s) within the analytical model. Bigger external loadings of a particular construct(s) designate a strong correlation among the significant variables for individual construct(s)
The rule of thumb applies that variables with insignificant external loadings < 0.4 should be regularly eliminated from the scale [64]. The adjusted and the initial external loadings for the entire constructs of the measurement model are illustrated in Figure 3. Consequently, all the external loadings apart from the red variables, as indicated in Figure 3, have been excluded from the original analytical model. The exclusion was due to lesser factor loadings < 0.50 and designated their less contribution to the significant concepts.

Discriminant Validity

Discriminant validity (DV) can be precisely described immediately; the model’s construct varies significantly from the remaining constructs by the observed standard. Consequently, establishing DV puts forward that the model’s construct(s) is typical and describes the singularities that were not adequately described by other construct(s) within the model [101]. The DV can be calculated via two discrete procedures: (i) Hetrotrait-Monotrait correlation ratio (HTMT) and (ii) Fornell-Larckers’s criterion.
The AVE’s square root of the individual model’s construct can be associated with the connections between one model construct and any other construct(s) to assess the DV. The AVE’s square root should be greater than the correlation among the concealed construct(s) based on the principles of Fornell and Larcker [73]. The results in Table 5 verify the analytical model’s DV [102].
Conversely, the Fornell and Larcker [73] criterion of typical DV has been disapproved by many researchers. Consequently, Henseler et al. [103] suggested a different procedure for DV evaluation, i.e., the HTMT is a novel procedure for DV evaluation of variance based-SEMs and calculates what should be the precise correlation among the binary model construct(s) if they were accurately calculated, i.e., if the model’s construct(s) are reliability consistent. The HTMT model was applied in this research to evaluate the DV. Hair et al. [76] suggested that the values of HTMT have to be below 0.85 and 0.90. it implies that the binary constructs were distinct. Suppose the model’s constructs are theoretically analogous. The HTMT values have to be below 0.90. Likewise, if the model’s constructs are theoretically atypical, the HTMT values must be below 0.85. The HTMT values for this study have been summarized in Table 6, and the model constructs have revealed adequate DV.

Validation of Path Model

After establishing the 4IR barriers as a determinative construct, the study further explores the collinearity between the objects of the formative construct by assessing the variable inflation factor (VIF) value. In this analysis, the entire VIF values fall below 3.5. it implied that these sub-domains have independently contributed to the higher-order concepts. Additionally, this study applied a bootstrapping technique to forecast the impact of the path coefficients. As indicated in Table 7, the path coefficients are statistically significant at 0.01 level, excluding insignificant readiness barriers [72]. Lastly, the final model is presented in Figure 4.

6. Discussion

As anticipated by scientists, the total adoption of 4IR technologies would increase the construction industry’s productivity [104,105,106]. This calls for a strong adoption of 4IR innovations to expand the building business’s implementation as the industry slowly recovers from the intense impacts of the Coronavirus pandemic (COVID-19) [107]. However, before full adoption can be realized, there is a need to identify and solve the barriers to 4IR innovations. This study revealed the barriers to 4IR innovation in three categories (policy and structure, readiness, and acquisition). These new classifications are discussed elaborately in the following paragraphs.
Policy and structure strongly influence the 4IR innovation adoption barriers (β = 0.924, SD = 0.045, p = 0.000), as revealed by the PLS-SEM result. This category of barriers contains thirteen barriers (the disjointed nature of the building business, limited availability of resources to the SMEs, knowledge and competency in computing, a limited number of skilled personnel, lack of protocols for coding objects, fear; cost of maintenance is very high; legal and contractual issues; high capital and setup cost; interoperability and compatibility; difficulty in understanding that new technology; resistance to change; and increased risk exposure) and accounts for 24.158% of the total variance of 4IR innovation barriers for sustainable development. Hence, H1 was accepted because the impact is significant. This barrier category has the highest impact on 4IR innovation adoption barriers. The construction industry and its entire supply chain are unpredictable and fragmented [108,109,110], resulting in failure and poor performance of construction projects. Manda and Ben Dhaou [111] also revealed that emerging economies face substantial developmental issues, including infrastructure and technological advancement. Adopting 4IR innovations can revolutionize the construction industry’s activities and ensure project success [112,113]. Similarly, Ibrahim et al. [114] revealed the critical factors affecting 4IR technologies adoption, including the technologies’ cost implication, stakeholders’ resistance to adoption, lack of awareness and inadequate standards and reference architectures. The findings from these studies agreed with the current findings, where standard, resistance, and cost issues belong to the category with the highest impact.
Readiness significantly influences the 4IR innovation adoption barriers (β = 0.161, SD = 0.064, p = 0.000). This category contains five barriers (training and learning, strategy and investment, organizational structure, quality of data and information; and Leadership) and accounts for 16.453% of the total variance of 4IR innovation barriers for sustainable development. Thus, H2 was accepted because the impact is significant. This barrier category has the second highest impact on 4IR innovation adoption barriers. Learning and education are significant components of adopting new technologies [106]. Construction industry stakeholders need to know how the technologies work before they can be fully implemented. Alade and Windapo [19] emphasized that leadership intelligence is critical to effectively adopting 4IR innovations. This is because 4IR innovation requires interaction between different discipline boundaries which requires a strong leadership component in 4IR. Researchers have also identified the organizational structure of companies as a critical barrier to 4IR innovation acceptance [109,115] due to its influence on information flow and decision-making in an organization.
Acquisition significantly influences the 4IR innovation adoption barriers (β = 0.148, SD = 0.054, p = 0.000). This category contains three barriers (difficulties of acquiring these technologies locally due to unavailability, Time spent on setting up, and Government policies) and accounts for 10.866% of the total variance of 4IR innovation barriers for sustainable development. Consequently, H3 was accepted because the impact is significant. This barrier category has the least impact on 4IR innovation adoption barriers. Although it has the least impact, it is still relevant to 4IR adoption. Previous studies in the 4IR domain have acknowledged the issues of learning new technologies due to the complexity, and high-level skill required [106,116,117] and acknowledged that the industrial revolution strongly impacted government policies. [118] further stressed that there is a need to ensure the proper framing of government policies for 4IR adoption to minimize the technology’s social, economic and environmental impact. In addition, from the results of this proposed model, it was revealed that “High capital and set-up cost” impedes the adoption of 4IR Innovations for construction projects the most. This is in tune with the study of Olojede et al. [119] which has it that a major barrier to the usage of 4IR innovation to housing and Service delivery in South Africa is high cost; and also that of Shi et al. [120] which shows that a there is a high correlation between the high cost of 4IR Innovations and it’s slow rate of adoption. Shi et al. [120] suggested that many countries and industries would earnestly adopt 4IR Innovations safe for its very pricey nature.
Other germane barriers as identified in this study are “Interoperability and Compatibility”, as corroborated by Ghane et al. [121] “fragmented nature on construction industry” as corroborated by Alade and Windapo [122]; “Limited number of skilled personnel” as resonated Ayentimi et al. [123]; “Difficulties in understanding the new technologies” which is in tandem with Oke et al. [124]. Generally, all barriers put forward in this study are very key, this was proven with their high average ratings. The least ranked barrier is “Legal and contractual issues”; and even this barrier returned a moderately average rating.

7. Conclusions

The study looked at the link between the significant hurdles to 4IR breakthroughs and long-term development. The study used a numerical exploration technique to gather input from construction industry specialists via a questionnaire. Using Exploratory Factor Analysis, the questionnaire responses were utilized to categorize the 4IR obstacles into policy and structure, preparedness, and acquisition (EFA). A prediction model was also created using Structural Equation Modelling-Partial Least Squares (SEM-PLS). It explained the link between the barrier categories and the impediments to the adoption of 4IR innovation for sustainable development. In addition, high capital and set-up cost and Interoperability and Compatibility are the biggest barriers impeding the adoption of 4IR Innovations for construction projects in the Lagos state construction industry. The barriers to the adoption of 4IR Innovations for construction projects in Lagos state are grouped into, Policy and Structure barriers, Readiness barriers and Acquisition barriers. The findings revealed that policy and structure are essential components of 4IR implementation that construction industry stakeholders must pay particular attention to. The 4IR innovations are critical to the sustainable development of the construction industry. Through the proposed model, this study revealed:
  • The 4IR innovation adoption barriers into three unique categories ([1] policy and structure, [2] readiness, and [3] acquisition).
  • The study further revealed policy, structure, and acquisition barriers as the two categories that significantly influence 4IR innovations adoption.
  • Policy and structure had the most substantial impacts—A bespoke empirical model based on PLS-SEM establishing the implications of the barrier categories on 4IR innovations adoption.
  • The model aims to create a structured approach for addressing the barriers impeding 4IR innovation adoption for sustainable development. Therefore, this study has many contributions which can have implications concerning the adoption of 4IR innovation.

7.1. Implications and Contributions

The bespoke PLS-SEM model shows the interactions between the barrier categories and the barriers to adopting 4IR innovations. The significant barriers are of utmost importance for stakeholders because they enable them to tailor effective strategies to address them and increase 4IR innovation for sustainable development uptake in the construction industry. For instance, the government could propose appropriate policies to create a conducive environment for 4IR to thrive. Similarly, education providers would be able to develop a quality approach to educate future construction industry leaders about 4IR innovation. This is because Leadership is a critical component of 4IR innovation success. In addition, this study contributed to the body of knowledge on 4IR in the following ways:
  • The PLS-SEM model is the first to predict the barriers to 4IR innovation adoption. This is the first study to present the interaction between these variables.
  • The study also explored the interaction between the barriers influencing 4IR innovation and the barrier categories.
  • In addition, the study classified the barriers into unique categories based on their significance. This is useful for policymakers to introduce strategic 4IR innovation policies capable of improving 4IR innovation adoption for the construction industry.

7.2. Limitations and Areas for Future Research

This study made a remarkable contribution to the body of knowledge. However, it is not without some potential drawbacks.
Firstly, in terms of geographical restrictions, the study is restricted to Nigeria, one of Africa’s developing countries. Future studies can consider other places in Nigeria and Africa to improve the generality of this research.
Secondly, future studies could explore the impact of policies on 4IR innovation adoption. This is because policy tends to influence 4IR innovation barriers strongly. Therefore, developing a novel policy framework for 4IR innovation adoption for the construction industry is imperative.
Thirdly, in terms of the sample size (n = 257) used for this study, future studies could explore the use of a larger sample size to improve the reliability of this study.

Author Contributions

Conceptualization, A.E.O.; methodology, All authors; software, A.F.K.; validation, All authors; formal analysis, O.O. & A.F.K.; investigation, All authors; resources, All authors; data curation, All authors; writing—original draft preparation, All authors; writing—review and editing, All authors; visualization, All authors; supervision, All authors; project administration, All authors; funding acquisition, All authors. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported via funding from Universiti Sains Malaysia. This study is supported via funding from Prince Sattam Bin Abdulaziz University project number 605 (PSAU/2023/R/1444). This study is supported via funding from Universiti Utara Malaysia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Mathematics 11 01003 g001 550
Figure 1. Research design.
Figure 1. Research design.
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Figure 3. PLS Initial Model.
Figure 3. PLS Initial Model.
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Figure 4. Path Model.
Figure 4. Path Model.
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Table
Table 1. Barriers to 4IR Innovation Adoption in the Construction Industry.
Table 1. Barriers to 4IR Innovation Adoption in the Construction Industry.
CodeNameStudies
B1Resistance to Change[42,43,44,45,46,47,48,49]
B2Fear[45,46,47,48,49,50,51]
B3Knowledge and competency in computing[47,48,50,51]
B4Difficulty in understanding the new technology[48,50,51]
B5Leadership[48,51]
B6Organisational Structure[43,45,46,47,48,50,52,53,54,55,56]
B7Training & Learning[48,50,51]
B8Strategy & Investment[48,51,55,57]
B9Quality of data & information[48,50]
B10Interoperability & Compatibility[48,51]
B11Limited availability of resources to SMEs[42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58]
B12High capital and setup cost[47,51]
B13Increased risk exposure[42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58]
B14A limited number of skilled personnel[47,51]
B15The fragmented nature of the construction industry[48,49,50,53,54,56,58]
B16Lack of protocols for coding objects[46,47,48,50,52,54]
B17Legal and contractual issues[47,49,52,54,55]
B18Government policies[43,46,48]
B19Difficulties in acquiring these technologies locally due to the unavailability[47,51]
B20The time spent on setting up is much[48,51,55,57]
B21The cost of maintenance is very high[43,44,45,46,48,51,53,55,57]
Table
Table 2. KMO and Bartlett’s Test of the Barriers to the adoption of 4IR Innovations.
Table 2. KMO and Bartlett’s Test of the Barriers to the adoption of 4IR Innovations.
KMO Sampling Adequacy Measure0.771
Approx. Chi-Square2462.539
Bartlett’s Test of Sphericitydf210
Sig.0.000
Table
Table 3. Rotated Component Matrix of the 4IR Innovations Adoption Barriers.
Table 3. Rotated Component Matrix of the 4IR Innovations Adoption Barriers.
FactorsComponent
F1F2F3
Component 1: Policy and Structure Barriers
B150.738
B110.698
B30.678
B140.667
B160.635
B20.623
B210.609
B170.560
B120.504
B100.490
B40.439
B10.405
B130.402
Component 2: Readiness Barriers
B7 0.833
B8 0.733
B6 0.729
B9 0.723
B5 0.440
Component 3: Acquisition Barriers
B19 0.702
B20 0.598
B18 0.578
Eigenvalues6.7644.6073.042
Total Variance Explained (%)24.15816.45310.866
Table
Table 4. The result of convergent validity.
Table 4. The result of convergent validity.
ConstructsCronbach’s AlphaComposite ReliabilityAVE
Policy and Structure0.8270.8740.537
Readiness 0.8240.8680.690
Acquisition0.7000.8130.596
Table
Table 5. The result of the Fornell-Larcker criterion constructs.
Table 5. The result of the Fornell-Larcker criterion constructs.
AcquisitionPolicy and StructureReadiness
Acquisition0.772
Policy and Structure0.1600.733
Readiness 0.0770.1690.831
Table
Table 6. The result of the Heterotrait-Monotrait ratio (HTMT).
Table 6. The result of the Heterotrait-Monotrait ratio (HTMT).
ConstructsAcquisitionPolicy and StructureReadiness
Acquisition
Policy and Structure0.236
Readiness 0.2090.248
Table
Table 7. Hypotheses, SD, and p values for the model.
Table 7. Hypotheses, SD, and p values for the model.
PathsBSDp Values
Policy and structure → 4IR barriers0.9240.0450.000
Readiness → 4IR barriers0.1610.0640.000
Acquisition → 4IR barriers0.1480.0540.000
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Singh, P.S.J.; Oke, A.E.; Kineber, A.F.; Olanrewaju, O.I.; Omole, O.; Samsurijan, M.S.; Ramli, R.A. A Mathematical Analysis of 4IR Innovation Barriers in Developmental Social Work—A Structural Equation Modeling Approach. Mathematics 2023, 11, 1003. https://doi.org/10.3390/math11041003

AMA Style

Singh PSJ, Oke AE, Kineber AF, Olanrewaju OI, Omole O, Samsurijan MS, Ramli RA. A Mathematical Analysis of 4IR Innovation Barriers in Developmental Social Work—A Structural Equation Modeling Approach. Mathematics. 2023; 11(4):1003. https://doi.org/10.3390/math11041003

Chicago/Turabian Style

Singh, Paramjit Singh Jamir, Ayodeji Emmanuel Oke, Ahmed Farouk Kineber, Oludolapo Ibrahim Olanrewaju, Olayinka Omole, Mohamad Shaharudin Samsurijan, and Rosfaraliza Azura Ramli. 2023. "A Mathematical Analysis of 4IR Innovation Barriers in Developmental Social Work—A Structural Equation Modeling Approach" Mathematics 11, no. 4: 1003. https://doi.org/10.3390/math11041003

APA Style

Singh, P. S. J., Oke, A. E., Kineber, A. F., Olanrewaju, O. I., Omole, O., Samsurijan, M. S., & Ramli, R. A. (2023). A Mathematical Analysis of 4IR Innovation Barriers in Developmental Social Work—A Structural Equation Modeling Approach. Mathematics, 11(4), 1003. https://doi.org/10.3390/math11041003

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