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Article

Barriers to Building Information Modeling (BIM) Deployment in Small Construction Projects: Malaysian Construction Industry

by
Ahsan Waqar
,
Abdul Hannan Qureshi
and
Wesam Salah Alaloul
*
Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2477; https://doi.org/10.3390/su15032477
Submission received: 22 December 2022 / Revised: 5 January 2023 / Accepted: 5 January 2023 / Published: 30 January 2023
(This article belongs to the Section Environmental Sustainability and Applications)
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Abstract

:
Building information modeling (BIM) application in construction projects is considered beneficial for effective decision making throughout the project lifecycle, as it maximizes benefits without compromising practicality. The Malaysian construction industry is also keen on the adoption of BIM culture. However, various identified and unidentified barriers are hindering its practical implementation. In light of this, this study identified and analyzed critical obstacles to using BIM in Malaysian small construction projects. Through the use of semi-structured interviews and a pilot study using the exploratory factor analysis (EFA) method, the critical BIM barriers (CBBs) have been identified. Based on the findings of the EFA, CBBs were classified into five categories, i.e., technical adoption barrier, behavioral barrier, implementation barrier, management barrier, and digital education barrier. Following the questionnaire survey, feedback of 235 professionals was collected with vested interests in the Malaysian construction business, and the CBBs model was created using analysis of moment structures (AMOS). The findings revealed that although Malaysian experts with little experience in practice were fairly educated about BIM, technical adoption barriers, behavioral barriers, management barriers, and implementation hurdles were critical for adopting BIM. The study’s findings will help policymakers eliminate CBBs and use BIM in Malaysia’s modest construction projects to save costs, save time, boost productivity, and improve quality and sustainability.

1. Introduction

Building information modeling (BIM) allows construction and design teams to maximize their existing technological infrastructure. By consolidating all relevant multi-disciplinary construction and design documents into a single repository, the BIM process facilitates the development and administration of data across the entire architecture engineering and construction (AEC) project lifecycle [1]. Oyuga et al. [2] described the application of BIM as reviewing and checking the daily on-site performance of work activities in comparison to the created plans and confirming the expected performance before or throughout the project. Moreover, Durdyev et al. [3] emphasized the BIM application in small construction projects to be essential, as it allows construction managers to make choices quickly and accurately based on critical inputs. Successful building projects correlate to how well BIM metrics are used. BIM has the capability to integrate with imaging (videogrammetry, laser scanning, and photogrammetry), geospatial (geographic information system (GIS) and global positioning system (GPS), ultra-wideband (UWB), radio frequency identification (RFID), and barcode), and virtual and augmented reality (VR/AR) technologies [4,5]. Hyarat et al. [6] identified that four-dimensional BIM models are necessary to monitor and analyze the building processes. In addition, BIM has been regarded as the first step toward digital construction and has been integrated with a wide range of construction operations, including facility elevations, prefabricated construction projects, and project management activities [7]. According to Olanrewaju et al. [8], one of BIM’s core tasks is effective progress management of construction operations, which was not possible due to the many challenges encountered throughout its adoption. According to Berges-Alvarez et al. [9], there is less room for error and less time to think things through when making sustainability-related decisions. A connection is made between the environment and the economy using BIM. A solid proof of concept may be used to push BIM software into the conceptual design phase. The method is only partially automated but nevertheless allows for well-considered choices to be made during the preliminary stages of a building’s design. In Olanrewaju et al. [10], the areas of uncertainty, omission and misuse in BIM-based projects have been identified.
In light of the above discussion, it is clear that further research into the dimensions and technical qualities determining the effective deployment of BIM is necessary to improve the knowledge and trust of stakeholders in the construction sector. According to Abu-Hamdeh et al. [11], the contemporary building business recognizes the need to enhance building energy efficiency and use cutting-edge technology. Lin et al. [12] clarified that the use of three-dimensional modeling has also been proven to be helpful in lowering buildings’ harmful effects on the environment. According to Chen et al. [13], the construction sector stakeholders’ resistance to embracing technology stems mainly from a need for knowledge of management system standards, requirements, and reference frameworks. The fourth industrial revolution (IR4.0) has accelerated the building industry’s transition to digital methods [14]. To realize the vision of a fully digitalized construction environment and to advance the IR4.0 environment, it is necessary to encourage the construction industry and other relevant stakeholders to adopt BIM systems for construction processes by addressing the uncertainties they may have about doing so [15].
Charef et al. [16], Shirowzhan et al. [17], Ahmed and Hosque [18] and Hamid and Embi [19] have identified the barriers to BIM implementation in construction projects without categorization on the basis of the scale of the project. It is indicated by the implications of Hamid and Embi [19] and Alwee et al. [20] that the challenges of BIM implementation are not always the same for small-scale and large-scale construction projects, even in the international context. The aforementioned facts provide a rationale for this study, and the scope has been narrowed down to small construction projects. No specific study, such as Taat et al. [21], Manzoor et al. [22], Belayutham et al. [23] and Chen et al. [13], has targeted the small construction projects from Malaysia indicating BIM barriers. For effective identification of barriers relative to any subject variable, there is always a need for non-parametric statistical evaluation and structural equation modeling (SEM), as indicated by Ringle et al. [24] and Wang and Rhemtulla [25]. According to Arif et al. [26] and Yaakob et al. [27], the Malaysian construction industry contributes majorly to the economy by which small construction projects must adopt modern technologies in which BIM is on top. Small construction projects cannot contribute generously to the Malaysian economy without considering the restructuring of small construction projects with modern construction technologies, including BIM. Following the gap indicated by the abovementioned studies, this research involves exploratory factor analysis (EFA) and SEM, which make it unique in specific Malaysian small construction projects.
To effectively deploy BIM technologies, this research intends to establish the research framework by addressing the theoretical-based technical constraints to adoption. The SEM methodology has been adopted to develop a conceptual framework for BIM barriers for each component and to highlight relevant BIM criteria. This research has focused on the localized obstacles to using BIM technology on modest building projects and how those obstacles might be overcome using different approaches. To help construction industry experts and stakeholders develop confidence in using innovative BIM technologies, this study has built the model taking BIM barriers for effective implementation on small construction projects before or after evaluation activities. The power of this research lies in its capacity to push this area of knowledge toward creating a foundational technical model that would facilitate the more effective use of BIM tools in Malaysia’s smaller building projects.

2. Methodology

The technique followed a step-by-step procedure that required thorough identification and evaluation of BIM hurdles in order to resolve the concerns discovered in the research. Figure 1 shows the overall study workflow pipeline adopted to achieve a conceptual model reflecting BIM barriers.
First, critical evaluation of the scholarly literature was performed to identify the obstacles toward BIM adoption in small construction projects considering the Malaysian construction industry. Challenges with BIM were identified, and then, those barriers were fine-tuned through semi-structured interviews with five BIM experts and seven experts in small construction projects in Malaysia.
For this study on the challenges of using BIM for small construction projects, it was required to develop a framework. Methods under consideration include multiple linear regression (MLR), structural equation modeling (SEM), system dynamics (SD), exploratory factor analysis (EFA), and artificial neural networks (ANN). According to Julian et el. [28], there is a relationship between unobserved variables; hence, the MLR was not selected. In accordance with Kiraly et al. [29], due to the nature of the research’s presented data, SD could not be used. According to Abiodun et al. [30], ANN is a prediction tool, and the purpose of this study is to analyze the difficulties associated with employing BIM on small construction projects. Using the SEM method, several observable and unobservable variables may be defined. SEM has proven to be a beneficial technique in the face of variable inaccuracy. In this study, the SEM approach was utilized to develop a model to identify and find the relationship between BIM barriers and small-scale construction projects. In the social sciences, SEM data are frequently used and acknowledged. Exploratory factor analysis (EFA) and reliability analyses are then used on pilot survey data to see whether any more barriers can be eliminated. At the end of the process, the primary survey with questionnaires was conducted. The confirmatory factor analysis (CFA) technique was used to develop a measurement model by performing convergent and discriminant validity for determining the most significant barriers to implementing BIM in Malaysia’s small construction projects.

2.1. Structured Literature Collection

The data-gathering process began with a thorough assessment of BIM barriers in the available literature. The primary goal was to identify the key BIM barriers affecting small building projects. Furthermore, the total strategy was built to find the most agreed-upon BIM hurdles by previous researchers since the critical assessment of current studies is essential for achieving any result. Information was gathered from six different sources, including Springer, Web of Science (WoS), American Society of Civil Engineers (ASCE), Science Direct, Multidisciplinary Digital Publishing Institute (MDPI), and Scopus, while keeping the research timeline between 2011 and 2022. Articles were searched using a number of different keyword combinations, with the flexibility of those keywords being adjusted to fit the overarching subject of the study. We looked for BIM barriers mentioned in previous research publications but restricted our scope to projects of small scale. With all possible keyword combinations, existing papers were searched from the perspective of BIM barriers. The number of studies gathered and relevant studies discovered in the aforementioned databases are summarized in Table 1.
With several keyword iterations, we were able to pull in a total of 2480 items from all databases. There were 248 papers that met the criteria for this study after examining their titles, abstracts, and potential barriers. With the goal of identifying BIM hurdles in small building projects, we performed a comprehensive literature study of 248 papers. The majority of barriers identified in the articles had common ground with those that were omitted. There were only 34 barriers that were found to be significant while dealing with small construction projects in Malaysia after the literature research was completed. Table 2 provides a summary of the data collected, including the various types of obstacles found and the categories into which they fall. Barriers to BIM were classified into the following categories, as determined by a review of the relevant literature: human resource barriers, technology barriers, safety barriers, regulatory barriers and financial barriers.

2.2. Qualitative Analysis (Interview)

For semi-structured interviews, a qualitative questionnaire was prepared involving barriers related to BIM implementation in Malaysian small construction projects. The qualitative questionnaire included all five categories of BIM barriers (human resource barriers, technology barriers, safety barriers, construction environment barriers and financial barriers) based on the BIM categorization from the literature. The required sample size for a semi-structured interview was reported differently in previous studies. Because of the descriptive nature of interviews, the purpose is always to collect as much information as possible. Time is also a factor that limits the number of people involved in interviews. According to literature, the minimum sample size for qualitative interviews must lie between 10 and 20 subjects. In contrast, Dworkin [195] suggested that the minimum number of experts involved in interviews should be between 5 and 50. Furthermore, Hesse-Biber [196] recommended that the minimum number of experts should be 10 with respect to sample size. As a result, 12 experts from Malaysia’s small construction industry stakeholders were invited for semi-structured interviews. Higher-level roles in projects, such as executives and project managers, were decidedly interviewed because the implementation of BIM falls under their responsibilities in any construction project. Based on the unavailability of three interviewees, they were interviewed online via conference call, and the remaining were interviewed via a face-to-face meeting.
From the interview, there was total disagreement among the interviewees on the BIM barriers such as inadequate access to decision-making resources (B24), the decision to utilize depends on the specifics of each case (B25), high ongoing investment in digital infrastructure (B26), inability to foresee digital technology’s positive effects on the safety management process (B27), the need for affordable digital tools hinders the safety management process (B28), inappropriate rate of return (ROR) and rate of investment (ROI) data (B29), productivity loss when adopting BIM in place of traditional construction (B30), lack of flexible modeling capability in BIM tools (B31), existing computer-aided design (CAD) tools are appropriate for work (B32), financial uncertainty related with BIM adoption (B33) and high risk of conflicts in construction contracts (B34). Out of 34 BIM barriers investigated in the interview, only 23 were identified by experts to be suitable for further investigation.
NVivo 12, a qualitative analysis software, was used to perform detailed content analysis and to categorize the words said by interviewees. The analysis found ten primary categories: complexity, cost, culture, digital adoption, expertise, interest, legislation, safety, safety management resources, and technology, as shown in Figure 2. A total of 23 parameters were extracted, divided into ten prime categories, from the content analysis, which were used to develop the final colligated framework involving all outcomes of the literature review and interview analysis, as shown in Table 3.

2.3. Quantitative Analysis (Pilot Survey and Main Survey Questionnaire)

According to the Construction Industry Development Board (CIDB), there were a total of 39,158 registered small construction companies in Malaysia in 2021, and around 80% of them are actively operating on small construction projects. Perak was selected as the research area in which construction companies from grades G1 to G4 were selected. A complete random sampling method was adopted to determine the sample size. A pilot survey was conducted on the 23 BIM barriers identified during interviews. A pilot questionnaire was constructed involving closed-ended questions based on 23 BIM barriers. The sample size was decided to be a minimum of 100 respondents, while the distributed pilot survey questionnaires were 200. Respondents were from small construction companies only operating in Malaysia. Out of 200 distributed pilot questionnaires, 166 were obtained, meeting the validity criteria of more than 50%. Exploratory factor analysis (EFA) was conducted on the obtained dataset of the pilot survey questionnaire. Rather than putting a predetermined structure on the data, EFA looks into whether or not the recommended combination of variables or characteristics is acceptable, and it also looks into the probable underlying factor structure of a collection of observed variables. EFA was a suitable test in this case because the sample size was between the 150 and 300 range, and the BIM barriers were found to be 23, which was in the acceptable range of 20 to 50. Furthermore, the sample size (166) should be greater than the product of the number of responses (5) and the number of survey questions (23). For this pilot survey questionnaire, 166 was greater than 23 × 5 = 115, which qualified the data for EFA analysis. Data were also subjected to the Kaiser–Mayer–Olkin (KMO) and Bartlett’s Tests to assess the representativeness and homogeneity of the sample. The KMO test has a range of 0–1 for its index, with results above 0.6 being considered satisfactory for revealing the character of correlations between variables. A p value of less than 0.05 for Bartlett’s Test, which evaluates the sphericity of data through factor analysis, is considered to be acceptable. SPSS 24.0 was used to conduct both EFA and KMO and Bartlett’s Test.
For the main questionnaire analysis by quantitative survey, the determined sample size is 240, while 100 is the minimum. A total of 20 BIM barriers were involved in the main questionnaire survey resulting from EFA. Demographics data were also collected to efficiently analyze the frequency of respondents. The questionnaire was distributed to 500 contractor companies in Malaysia working on small construction projects. SEM was performed for analytical purposes. In order to evaluate hypotheses about the connections between latent variables and the observed data, SEM was created in the 1980s. The first model in SEM is the measurement model, and it employs confirmatory factor analysis (CFA) to enrich the model by confirming the validity and reliability of the measuring variables against pre-set criteria, consequently linking the constructs with the latent components. The second model, a structural model, evaluates the relationships between the latent components by computing variances, testing hypotheses, and changing the model as necessary. By swapping out the correlation between the components for the hypothesized causal links, the conceptual model may be fine-tuned until it can be used to test the hypothesis. This study developed a conceptual framework for SEM evaluation using the findings of an EFA analysis on the previously identified barriers to BIM collected from the literature.

3. Analysis and Discussion

3.1. Background Information of Respondents

The background information of respondents is presented in Table 4. From a professional perspective, 7.23% were architects, 8.43% were quantity surveyors, 59.64% were civil engineers, 4.22% were M&E engineers, 18.07% were project managers, and the remaining 2.41% were from other profession types. A high percentage of civil engineers were involved in the study, which corresponds to the better judgement of BIM implementation barriers in small construction projects. From an organization perspective, 48.19% of the respondents worked in contractor organizations, 45.78% worked in consultant organizations, and the remaining 6.02% were directly working with the client. From experience in the Malaysian construction industry perspective, 23.49% had 0–5 years, 31.33% had 10–15 years, 6.63% had 15–20 years, and 4.82% had over 20 years of experience. More young workers involved in the study were people dealing with the implementation of BIM. It was also found that 99.40% of the respondents were working on small construction projects, and the remaining 0.6% were not relevant to small construction projects in Malaysia.

3.2. Level of Frequency of BIM in Small Construction Projects

The frequency of BIM implementation in small construction projects is presented in Figure 3. By following Halim et al. [197] and Hyarat et al. [6], the five levels were used to measure the response. According to the results, 43% of the respondents indicated very low implementation of BIM in small construction projects in Malaysia. Moving further, 28% of the respondents indicated low, 6% indicated average, 10% indicated high, and 13% indicated very high implementation of BIM in small construction projects in Malaysia. If seen from the collective perspective, the disagreement regarding the implementation of BIM in small construction projects is 71%, while the agreement is only 23%. It can be interpreted from the results that Malaysia does not have a significant implementation of BIM in small construction projects. Oslanrewaju et al. [8] also indicated the lack of the latest construction technologies in developing countries such as Malaysia. Similar findings were obtained from a primary research perspective showing that the Malaysian small construction industry still lacks BIM. It verifies the research gap and provides adequate comparative insights with existing research where the BIM implementation in small construction projects was found to be very low [20,198].

3.3. Barriers to BIM in Small Construction Projects

3.3.1. Reliability and Normality of Data

Whole data from the questionnaire were tested for reliability by measuring Cronbach’s Alpha value. The initial test on the reliability indicated a Cronbach’s Alpha above 0.8, which indicated high reliability of data. Further interpretation of the reliability constant for each of the BIM barriers indicated high reliability. The Shapiro–Wilk test was carried out to measure the significance of each barrier from a reliability perspective, as shown in Table 5. All the values were less than 0.05, indicating a high significance of data. With high reliability and significance, the test statistics confirmed further use of nonparametric tests.

3.3.2. Mean Score Ranking of BIM Barriers

By following the descriptive statistics, the mean score for each of the BIM barriers was calculated. The purpose was to determine if any BIM barriers have a mean score of less than 3. The lowest mean observed was 3.05, and the maximum was 3.49. Both are greater than 3, which confirms that the mean of data is completely fine, and there is no irregularity in the data. Table 6 shows the mean score ranking of barriers to BIM in small construction projects showing mean, standard deviation (SD), rank, median and Kruskal–Wallis test results for intergroup comparisons. Moreover, the Wilcoxon signed-rank test was conducted to determine the significance of responses with respect to the sample mean values. The findings were satisfactory and indicated that all the BIM barriers are appropriately considered concerning the judgement of respondents and the research objectives.
The Kruskal–Wallis test was conducted for intergroup comparison based on profession, organization, experience and level of frequency of BIM in small construction projects. The classification of respondents was different in each group corresponding to the data collected in the demographics section of the questionnaire [8,21]. The interdependent groups are present in the data, which paved the way for choosing this test and determining the significance value to validate that the data are not normally distributed [26]. Significant results were produced for 14 barriers in different intergroup comparisons. Values larger than 0.05 indicated that the respondents from different professions, organizations, experience and perceived frequency of BIM in small construction were working under similar circumstances. Values lower than 0.05 indicated variation in the perception of BIM barriers with respect to their distribution in groups. The perceived values from the perspective of experience indicated more significant results. This confirms that experience influences people to understand more about the BIM barriers in small construction projects in Malaysia. This further strengthens the concept that the BIM implementation is not only dependent on some legislative measure, but the experienced professionals in small construction projects of Malaysia widely accept the barriers. Similar circumstances in the work environment pave the concept of facing similar kinds of barriers in small construction projects, as demonstrated by Kruskal–Willis test statistics. The values were found to be less deviated from the mean score obtained, confirming the agreement on BIM barriers considered in analysis.
According to calculated rank from descriptive mean analysis, the five most crucial barriers were found that are significantly affecting the implementation of BIM in small construction in Malaysia. These were B19 “High cost of BIM implementation” (mean = 3.49, rank = 1), B06 “Reluctance to transition to BIM” (mean = 3.402 rank = 2), B16 “High diversity of the workforce in projects” (mean = 3.40, rank = 3), B09 “Possibility of delays in construction” (mean = 3.39, rank = 4) and B03 “Lack of BIM experts” (mean = 3.31, rank = 5). In reality, BIM applications involve significant work and operations that do not always relate to the requirements of small construction projects. Durdyev et al. [3] stated that small construction projects mostly have operations in which the integration of BIM applications is inefficient because BIM tools are made commercially for heavy construction projects. Construction workers face problems when integrating BIM tools for small construction projects, where they cannot even find the BIM modules that could solve the problem effectively in small construction projects. B06 “Reluctance to transition to BIM” validated the ongoing trend in the construction sector of Malaysia where construction professionals are resultant to change their construction methods. They always want to stick with conventional methods because implementing new technologies such as BIM requires more resources and input from construction practitioners, which may not always be feasible [20]. This reluctant behavior contributed to putting another barrier in implementing BIM in Malaysia’s small construction projects. B16 “High diversity of the workforce in projects” indicated that construction professionals are not implementing BIM, as it is a time-consuming process for small construction projects. In small construction projects, procurement needs to be performed on a timely basis because time is short, and construction professionals always want to start the work as soon as possible [127]. This behavior creates a barrier to the implementation of BIM because construction professionals do not spend time getting into difficulties associated with the time-consuming aspect of BIM. B09 “Possibility of delays in construction” indicates that BIM implementation in small construction may increase the possibility of delays in projects. These delays are not acceptable in any case for small construction professionals because it places profits at stake. Any possible difficulty while working with BIM can easily create problems in the schedule of projects. B03 “Lack of BIM experts” indicates that experts are always needed to implement BIM in small construction projects in Malaysia. This is because many construction workers in small projects do not have experience working with BIM [197]. It makes it difficult and uncertain for construction workers to adopt BIM in all construction operations. B “Neither a simple nor universal strategy for BIM usage exists.” (mean = 3.05, rank = 21) was found to be the barrier with the lowest impact on implementation of BIM in small construction projects of Malaysia. It is understandable that BIM does not always require a universal method to be implemented in small construction projects because the requirements are totally different. A universal method can complicate the processes but can also help, depending on the situation where BIM is implemented. It also indicates a positive attitude present among the construction professionals because they are not demanding universal BIM methodology, and therefore, B12 cannot be taken as a significant barrier.

3.3.3. Factor Analysis of BIM Barriers

From the existing literature, 21 BIM implementation barriers were found, and statistically obtained data after survey analysis were significant. However, the possibility of having a similar impact on each barrier cannot be ignored. EFA can solve this problem and perform the grouping to some subgroups of barriers that can be practically feasible to explain concerning small construction projects in Malaysia. Suitability for EFA analysis was determined before conducting the analysis and obtaining subgroups of barriers. According to Al-Aidrous et al. [198] and Alwee et al. [20], the EFA should be conducted when the sample size is greater than 150 but less than 300. Further, it is also necessary to have a greater sample size than the number of questions multiplied by the number of responses each question has in the quantitative survey. For this study, that number is 105, less than 166, which is the sample size. The number of variables being employed in factor analysis must be at least 20 and greater than 50. The criteria for factor analysis are met; therefore, the factor analysis was conducted on 21 variables corresponding to BIM barriers, excluding the risk of inaccurate factor analysis results. The subject-to-variable ratio was found to be 7.90:1.00. Greater than 5:1 is required, and the validity of results from factor analysis is further confirmed [26].
Kaiser–Mayer–Olkin (KMO) and Bartlett’s test were applied to the data of 21 variables to measure sampling adequacy and sphericity. The index range of the KMO test is from 0 to 1, in which the acceptable results lie above 0.6, telling the nature of correlations among the variables [199]. For Bartlett’s test, the required significance value should be less than 0.05 for good factor analysis results, measuring the sphericity of data. SPSS 24.0 was used, and the findings are presented in Table 7 for both tests. KMO index was found to be 0.853, which is greater than 0.6 and is therefore acceptable. The significance of Bartlett’s test was found to be 0.000, which is less than 0.05, indicating that EFA can be adopted for making a subgroup of variables considered in this study.
Principal component analysis (PCA) was used to conduct EFA analysis, and factor structure was obtained for the 21 variables. Varimax rotation was applied to obtain the rotated component structure. EFA results are presented in Table 8, from which five components have an Eigen value greater than 1. The scree plot presented in Figure 4 indicates the same behavior of variables involved in the analysis. The first five components on the x-axis have an Eigen value greater than one, indicating the possible division of BIM barriers in four groups. The cumulative variance obtained for the five groups is 50.878%, which is greater than 50% and indicates acceptable components. The minimum factor loading cutoff limit of 0.4 was applied to obtain the results corresponding to the rotated component structure.
After examining the component structure obtained from EFA, the five subgroups were devised based on the number of components. They were named behavioral barriers, technical adoption barriers, management barriers, implementation barriers and digital education barriers. The corresponding mean of each barrier in the subgroup was used to calculate the mean for each subgroup. Table 9 shows the mean score ranking of the BIM barriers subgroup, indicating barriers, subgroup mean and subgroup rank of all subgroups. The final ranking of BIM barriers was performed based on the mean subgroup score, and it is discussed as follows.
Management Barriers (mean = 3.31, rank = 1,): The first-ranked subgroup consists of barriers related to management issues that construction workers face when implementing BIM in small construction projects in Malaysia. In total, 28.015% of the variance is explained by this subgroup. The specific items in this subgroup are B18 “Impact of COVID-19 on small construction projects”, B20 “The integration of BIM will change present levels of efficiency, and B16 “High diversity of the workforce in projects”. The overall impact of management barriers is strong from the perspective of affecting the implementation of BIM in small construction projects in Malaysia. It is a reality that after the COVID-19 pandemic, the situation of small construction companies was not favorable for adopting new technology, which acted as one of the management barriers to implementing BIM [128,199]. Further, Malaysia’s work environment is diverse, as most of the workers employed by the small construction companies are from other nations such as Bangladesh and India. The diverse workforce makes it very difficult for construction companies to manage the new technology’s implementation, which further creates a major barrier to implementing BIM [72,76]. Implementing BIM is also found to be very time-consuming by the latest research. When small companies try to implement a BIM, it creates unexpected delays in the project schedule. Similarly, leadership issues are always present from the senior management perspective, which contributes to decreasing the adoption rate of BIM by small construction companies.
Technical Adoption Barriers (mean = 3.29, rank = 2): The second-ranked subgroup consists of barriers related to technical issues that construction workers face implementing BIM in small construction projects. In total, 6.041% of the variance is explained by this subgroup. The specific items in this subgroup are: B11 “Absence of a structured methodology that is supportive”, B7 “Impractical theoretical evidence from research”, B2 “No facilitation and training center for BIM”, B21 “Poor BIM ability to integrate with project operations”, B13 “Lack of legal regulations”, B19 “High cost of BIM implementation”, B4 “Inadequate working processes and quality control standards” and B8 “Lack of awareness about the benefits of BIM”. The technical barriers are ranked second because most difficulties with implementing the BIM are related to ineffective management controls. Existing methodologies for implementing BIM in construction projects must fully support small construction companies. Further, the literature must provide evidence of practically improving BIM implementation in Malaysia’s small construction industry [122,200]. This is because the current environment is changing rapidly after 2020, and the existing protocols in construction may only sometimes work. Similarly, the lack of awareness contributes to increasing the technical difficulties while there is no existing mechanism for training to bridge the gap between large and small construction projects [72,76]. The nature of the client is also relevant to maintaining the project’s efficiency on a low budget, due to which they only sometimes demand the implementation of BIM. The integration difficulties are also present because of an inappropriate way of integrating BIM with project operations [200]. This will raise the cost of implementing BIM and impose obligations on small construction firms that need to be more technically prepared to implement it.
Implementation Barriers (mean = 3.27, rank = 3): The third-ranked subgroup consists of barriers related to practical adoption difficulties existing on the construction sites of small projects in Malaysia. In total, 5.847% of the variance is explained by this subgroup, and the specific items are: B9 “No financial support for small construction projects”, B10 “Possibility of delays in construction”, B5 “Insufficient teamwork from upper management” and B17 “Too much complexities in design produced by BIM”. In terms of implementation, a corporation is always required by the leadership, which unfortunately only happens in small construction companies, contributing to the implementation barrier. The risk of delay always exists, due to which the implementation can become uncertain and can even create ambiguity among the responsible workers in decision making [129]. Implementation can also be difficult because there needs to be more financial support available for small construction companies, which is relevant to government policy. The complexities in design also act as implementation barriers because they combine with other factors, such as a lack of awareness among the project members, which ultimately increases the problems in implementing the BIM.
Behavioral Barriers (mean = 3.24, rank = 4): The fourth-ranked subgroup consists of barriers related to behavioral difficulties that construction workers face when implementing BIM in small construction projects of Malaysia. In total, 5.665% of the variance is explained by this subgroup, and the specific items are: B15 “The lack of demand for or insistence on BIM from customers”, B14 “In the workplace, resistance to BIM adoption remains strong”, B6 “Aversion to adopting BIM”, B3 “Aside from the construction team leader, no other team members need BIM.” and B12 “Neither a simple nor universal strategy for BIM usage exists”. The subcontractor support is greatly affected when the implementation is not set according to plan for small construction projects, and ultimately, it sets a very inappropriate tone between the project stakeholders [122]. Workers in construction projects do not want to change the existing environment, which has direct consequences in increasing the behavior barrier. Most workers need to be more skilled in understanding the requirements of implementing BIMs in construction projects, which causes problems if a universal method is available.
Digital Education Barriers (mean = 3.23, rank = 5): The fifth-ranked subgroup consists of barriers relevant to poor digital education. In total, 5.311% of the variance is explained by this subgroup, and it only has one item: B1 “Lack of digital education and training”. Because many tools are available that have reduced the complexities of adopting BIM, digital education may only sometimes be required to understand the requirements of implementing BIM in small construction companies. As a result, the “digital education barriers” subgroup harms Malaysia’s environment.
Five subgroups were found from the mean square analysis impacting the implementation of BIM in small construction projects in Malaysia. A clear understanding was developed from the analysis regarding the rank of each subgroup in affecting the implementation of BIM.

3.4. Quantitative Survey

EFA variables having cross-loadings or loadings less than 0.4 were not included; afterward, the main questionnaire was developed, 235 individuals completed the main questionnaire, and new data were gathered. By using AMOS 22, CFA is used to assess the conceptual framework’s validity and dependability (CV-DV). In the CFA, the observed variables with loadings below 0.6 were eliminated. The measurement model’s final fit for the BIM barrier and parameters for the effective application are shown in Figure 5. Four constructs: “Technical Adoption Barriers (TAB),” “Behavioral Barriers (BB),” “Implementation Barriers (IB) and “Management Barriers (MB)”, were used to group the final refined parameters/variables. Variables B1 and B19 were removed from the finished framework since they had low factor loadings between the observed variable and the construct on CFA. Figure 5 illustrates the measurement model involving four groups, BB, TAB, IB and MB and 16 BIM implementation barriers. The value of significance for all barriers was significant, as they are above 0.6. The intergroup correlation is moderately significant but still above 0.3, indicating a high acceptability of outcomes.
In the model, for the improvement, error correlations were established for the variables B14–B15; however, correlated variables are unique parameters and have no similarity. Table 10 shows the reliability and validity tests for the measurement model. The goodness of fit (GOF) is shown in Table 11 and Table 12 for the measurement model and structural model, respectively. Based on the model fit of the measurement model, the structural model (SM) was developed, as shown in Figure 6. All values of correlations were significant between the barriers and their associated group. Further, the values were significant even between the latent variable and all categories involved in the structural model. The most significant group of BIM implementation barriers was BB, involving the maximum number of variables with high significance.

4. Discussion

Using a combination of a literature review, semi-structured interviews, and questionnaire surveys, the authors of this study developed knowledge-based standards and a reference model that highlights barriers to workers’ adoption of building information technology in Malaysia’s small construction projects. In addition, SEM was used to focus on and eliminate the most significant obstacles for a smooth BIM rollout. This research aimed to provide a theoretical framework that would emphasize existing challenges to implementing BIM processes. A workable conceptual framework was attained by analyzing and refining 23 SM parameters across five constructs to 16 general characteristics across four constructs. This is especially true given that some parameters appear conceptually similar but actually differ based on technical considerations. Figure 7 illustrates a framework emphasizing the overarching challenges of using BIM successfully in building projects. The framework includes all identified significant BIM barriers divided into groups by which the small construction industry cannot adopt BIM. The framework’s parameters and stumbling blocks are now technology agnostic. They were simplified by consolidating similar parameters under a single construct or by eliminating them entirely. According to the systematic literature assessment results, few researchers have only aimed to identify even the most fundamental technological hurdles in the BIM-based construction industry. However, performed research has focused on the primary hurdles in BIM technologies on building processes using key performance indicators or BIM following SEM.
The overarching goal of this model is to demonstrate familiarity with the obstacles that prevent BIM technology from being used effectively. Stakeholders in BIM technology and its application can use this model as a general reference. Studies by Arif et al. [26] and Sriyolja et al. [76]. are just two examples of the many that have evaluated the effects of digital technologies on the efficiency of construction operations by comparing performance- or ranking-related factors using the relative importance index (RII) technique. Therefore, in comparison with the aforementioned studies, this conceptual framework has been devised via performing a mathematical modeling technique, i.e., SEM, which underlines the precise variables and barriers related to the BIM process, to gain confidence in its application, basic operational guidelines, and to educate construction industry stakeholders. The model is novel since it is simple to grasp by practitioners, yet it addresses the broad factors that are genuine roadblocks to digital construction operation (BIM) efficiency. While King et al. [121], Hedayati et al. [78] and Taat et al. [21] have identified BIM roadblocks throughout Malaysia’s construction sector as a whole, their implications suggest that future studies conducted on a more granular scale might provide quite different results. Because of this, the results of this research vary when applied to the setting of solely Malaysian small building projects. Additionally, the findings from Taat et al. [21] and Belayutham et al. [23] do not employ the identical methods as in this study to identify the BIM barriers. Given that this study was limited in scope to very small building projects, it stands to reason that the results are highly distinctive.

4.1. Managerial Implications

Identifying significant BIM barriers may facilitate the development of a method that stakeholders such as project owners and contractors may use to integrate BIM into their small construction projects better. In addition, these small construction projects in Malaysia may make great progress by tackling the identified BIM barriers. This will replace the usual method of construction in Malaysian small construction projects. Small construction enterprises in Malaysia must apply BIM in order to have a lasting impact on the economy since the economy is often related to the success of the small construction sector. If the construction sector continues to expand, Malaysia may be able to enter the top 20 economies in the world. These research findings may potentially be utilized to encourage the use of BIM in other developing nations with comparable adoption rates for construction projects. This is particularly relevant in adjacent countries and the global context, where smaller construction projects will be better suited to focus on solving particular barriers. Therefore, countries with difficulty adopting BIM for small construction projects may benefit from employing BIM. Nonetheless, this study provides an enormous contribution that has significant implications for small construction project businesses in the following ways:
  • It offers a collection of knowledge on the BIM barriers that small construction industries are currently facing.
  • It assists small construction project owners, consultants, and contractors in analyzing and selecting the most effective BIM implementation to enhance project planning, efficiency, and consistency.
  • Presented are factual data that might benefit Malaysia and other countries in effectively using BIM for small construction projects.
  • In Malaysia, no research has been undertaken on the usage of BIM. This research is important because it reveals a connection between BIM hurdles and Malaysia’s small construction industry. This establishes a good platform for a discussion on how BIM might be used to enhance the safety of low-cost construction projects and overcome the knowledge gap.
  • The findings given here are relevant only to BIM implementation in small construction projects. Consequently, the project’s stakeholders may collaborate to overcome the BIM-related cost, time, and efficiency concerns. Achieving a high degree of sustainability in a project has positive long-term implications.
  • This study also establishes a benchmark for measuring the effectiveness of BIM in the administration of a small building project.
  • Local communities will be positively affected by the outcomes of this study, as BIM will help in increasing project efficiency and ultimately move small construction projects toward sustainability for Malaysian society.

4.2. Theoretical Implications

Although BIM has been available for a while, its significance is growing even for relatively small construction projects. Small construction projects, in particular, are highlighted by the proposed BIM barriers framework as requiring BIM adoption. This study uses the proposed model to shed insight on the challenges that prevent the use of BIM. These challenges actually work in favor of bringing BIM to Malaysia’s relatively small construction industry. Thus, the findings of this study will assist in closing the gap between theoretical and practical BIM implementation. We are not aware of any research that has looked at the barriers to using BIM in the Malaysian construction industry. This finding provides a starting point for researchers, particularly those in the field of construction management, to examine the difficulties of BIM in the context of the small construction industry. Because of this, the theoretical outcomes of this study give a mathematical foundation for precisely recognizing the barriers of BIM, which might be effectively implemented in Malaysia and abroad. The results will be under fair principle, as the study is aimed to improve the implementation of BIM in small construction projects. The results can be used by future researchers in any possible way to improve the implementation of BIM in small construction projects.

5. Conclusions

This research aims to identify and highlight the most fundamental factors or barriers preventing the widespread adoption of building information technology in Malaysia’s smaller-scale building projects. In this study, a structured literature review analysis was used with a systematic approach to review the literature and to choose the papers that would be included. After reviewing the data, 34 barriers were singled out as particularly troubling for using BIM in Malaysian small building projects. After conducting semi-structured interviews and evaluating the data using NVIVO, we narrowed our list down to 23 barriers. Afterward, a survey was conducted, and an EFA was performed on the collected data. Following this, the main questionnaire was developed to capture more relevant data from the industry experts and academia. Based on the replies, a structural equation modeling (SEM) technique was selected for statistical analysis, with a specific emphasis on the characteristics of BIM implementation that are hindered by barriers. After conducting SEM tests, the model was updated to reflect the factors that have led many to conclude that BIM is not useful for less-scaled building projects. The conceptual framework established by EFA represents the 16 aspects affecting the deployment of the BIM and is based on a statistical study of 23 parameters. Later, applying convergent and discriminant reliability (CFA), 16 variables were left that accurately reflected the most pressing issues preventing the adoption of BIM in Malaysia’s small construction industry. These variables were organized into four categories: technical adoption barriers; behavioral barriers; management barriers; and implementation barriers.
This research uniquely contributes to the current literature by identifying obstacles to BIM’s use in Malaysia’s smaller building projects. The results are useful for closing the knowledge gap between the existing theoretical literature and the actual use of BIM by the Malaysian small construction sector. Because of the specificity of the study’s methodology, sample size, industry size, and possible stakeholders, its findings can only be applied to the small construction sector. The stakeholders better grasp the overall barriers to the deployment of BIM with this model, which depicts the factors that enable the proper implementation of BIM. This study’s systematic literature evaluation revealed that few prior investigations into the challenges of implementing BIM in Malaysia used appropriate SEM analytic methods and procedures. Therefore, this research aimed to provide a knowledge framework to close the information gap that contributes to stakeholders’ skepticism in the construction sector toward technology. The completed model will persuade construction business professionals to use BIM tools, aiding the IR 4.0 ecosystem and saving money in the long term. This research contributes to the theory and practice for adopting BIM in small construction projects; however, limitations and future research opportunities exist for this study. The study included a sample population only from Perak, Malaysia; the scope of the study can be widened by considering other states or countries, as more factors can be identified. In addition, key factors may vary for other countries, as such factors are dependent on the construction environment, practices, and technological culture. Future studies can be conducted by adopting a more advanced quantitative research method, and effective mitigation techniques can be devised for individual BIM barriers presented in the final framework of this research. Moreover, SM can be modified in terms of project performance control or key performance indicators (cost, time, and quality), primary or secondary processes related to the project (safety management, project planning, supply chain management, etc.), and external implications (CO2 emissions), considering the barriers to BIM implementation in aforementioned processes.

Author Contributions

Conceptualization, W.S.A. and A.W.; methodology, A.W.; software, A.H.Q. and A.W.; validation, W.S.A., A.H.Q. and A.W.; resources, W.S.A.; data curation, A.W. and A.H.Q.; writing—original draft preparation, A.W.; writing—review and editing, W.S.A. and A.H.Q.; supervision, W.S.A.; funding acquisition, W.S.A. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to appreciate the YUTP-FRG 1/2021 (015LC0-369) in Universiti Teknologi PETRONAS (UTP) awarded to Wesam Alaloul for the support.

Data Availability Statement

All data, models, and code generated or used during the study appear in the submitted article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study flow chart and the successive stages.
Figure 1. Study flow chart and the successive stages.
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Figure 2. Qualitative analysis outcomes via NVivo.
Figure 2. Qualitative analysis outcomes via NVivo.
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Figure 3. Level of BIM in small construction projects.
Figure 3. Level of BIM in small construction projects.
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Figure 4. Scree plot results.
Figure 4. Scree plot results.
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Figure 5. Measurement model for BIM implementation barriers.
Figure 5. Measurement model for BIM implementation barriers.
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Figure 6. Structural model for BIM implementation barriers.
Figure 6. Structural model for BIM implementation barriers.
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Figure 7. BIM barrier-based framework for small construction projects in Malaysia.
Figure 7. BIM barrier-based framework for small construction projects in Malaysia.
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Table
Table 1. Data collection summary.
Table 1. Data collection summary.
DatabaseKeywords CombinationTotal Collected StudiesRelevant Studies
Springer“Small Construction Projects AND BIM OR Challenges or Barriers in BIM Adoption in Small Construction Projects”82744
WoS“BIM Challenges AND Small Construction Projects OR BIM Adoption Barriers in Small Construction Projects”23035
ASCE“Building Information Projects OR BIM in Small Construction Projects OR BIM Barriers OR BIM Challenges”45621
Science Direct“BIM AND Small Construction Projects OR BIM Problems AND Small Construction Projects”15387
Scopus“BIM Challenges OR BIM Hurdles in Small Construction OR Building Information Modeling”246
Google Scholar“Building Information Modeling OR BIM OR Barriers AND Challenges OR Small Construction Projects”79055
Table
Table 2. Identified BIM barriers and related details.
Table 2. Identified BIM barriers and related details.
CategoriesCodingParametersSources
Human Resource Barriers
Technology Barriers		B1Lack of digital education and training[31,32,33,34,35]
B3Aside from the construction team leader, no other team members need BIM[36,37,38,39,40]
B5Insufficient teamwork from upper management[41,42,43,44]
B8Lack of awareness about the benefits of BIM[45,46,47,48,49,50,51]
B9No facilitation and training center for BIM[18,20,52,53,54,55,56,57,58]
B16High diversity of workforce in projects[59,60,61,62,63,64]
B22Not enough expertise in safety management[65,66,67,68]
B34High risk of conflicts in construction contracts[69,70,71,72]
B21Poor BIM ability to integrate with project operations[73,74,75,76,77,78]
B23The construction industry’s lackluster adoption of technology[79,80]
B31Lack of flexible modeling capability in BIM tools[81,82,83,84,85]
B32Existing computer-aided design (CAD) tools are appropriate for work[69,70,71,72]
Safety BarriersB27Inability to foresee digital technology’s positive effects on the safety management process[8,22,86,87,88]
B28The need for affordable digital tools hinders the safety management process[16,89,90,91]
Construction Environment BarriersB4Inadequate working processes and quality control standards[26,92,93,94,95]
B6Aversion to adopting BIM[96,97,98,99,100,101,102]
B7Impractical theoretical evidence from research[23,103,104,105,106,107]
B10Possibility of delays in construction[17,108,109]
B11Absence of a structured methodology that is supportive[110,111,112,113,114]
B12Neither a simple nor universal strategy for BIM Usage exists[115,116,117]
B13Lack of legal regulations[118,119,120,121]
B14In the workplace, resistance to BIM adoption remains strong[122,123,124,125,126,127,128,129]
B15The lack of demand for or insistence on BIM from customers[21,130,131,132,133]
B17Too many complexities in design produced by BIM[27,134,135,136,137,138]
B20The integration of BIM will change present levels of efficiency[139,140,141,142,143,144]
B24Inadequate access to decision-making resources[145,146,147,148,149,150,151]
B25The decision to utilize depends on the specifics of each case[152,153,154,155,156]
Financial BarriersB2Competition is high, and profit margins are low[157,158,159,160,161]
B18Impact of COVID-19 on small construction projects[3,162,163]
B19High cost of BIM implementation[19,164,165,166,167,168,169,170,171]
B29Inappropriate rate of return (ROR) and rate of investment (ROI) data[172,173,174,175]
B26High ongoing investment in digital infrastructure[2,176,177,178,179,180,181,182,183,184]
B30Productivity loss when adopting BIM in place of traditional construction[13,185,186,187,188]
B33Financial uncertainty related with BIM adoption[6,189,190,191,192,193,194]
Table
Table 3. Final colligated framework for BIM barriers.
Table 3. Final colligated framework for BIM barriers.
CategoriesCodingParametersSources
ComplexityB25The decision to utilize depends on the specifics of each caseDeleted
B31Lack of flexible modeling capability in BIM toolsDeleted
B32Existing computer-aided design (CAD) tools are appropriate for workDeleted
B34High risk of conflicts in construction contractsDeleted
B10Possibility of delays in constructionMaintained
B4Inadequate working processes and quality control standardsMaintained
CostB2Competition is high, and profit margins are lowMaintained
B19High cost of BIM implementationMaintained
B7Impractical theoretical evidence from researchMaintained
B13Lack of legal regulationsMaintained
B26High ongoing investment in digital infrastructureDeleted
B29Inappropriate rate of return (ROR) and rate of investment (ROI) dataDeleted
B30Productivity loss when adopting BIM in place of traditional constructionDeleted
B33Financial uncertainty related with BIM adoptionDeleted
CultureB23The construction industry’s lackluster adoption of technologyMaintained
Digital AdoptionB18Impact of COVID-19 on small construction projectsMaintained
B24Inadequate access to decision-making resourcesDeleted
ExpertiseB15The lack of demand for or insistence on BIM from customersMaintained
B9No facilitation and training center for BIMMaintained
B22Not enough expertise in safety managementMaintained
InterestB17Too many complexities in design produced by BIMMaintained
B21Poor BIM ability to integrate with project operationsMaintained
LegislationB16High diversity of workforce in projectsMaintained
B20The integration of BIM will change present levels of efficiencyMaintained
SafetyB8Lack of awareness about the benefits of BIMMaintained
B12Neither a simple nor universal strategy for BIM Usage existsMaintained
B27Inability to foresee digital technology’s positive effects on the safety management processDeleted
Safety Management ResourcesB1Lack of digital education and trainingMaintained
B5Insufficient teamwork from upper managementMaintained
B28The need for affordable digital tools hinders the safety management processDeleted
TechnologyB3Aside from the construction team leader, no other team members need BIMMaintained
B6Aversion to adopting BIMMaintained
B11Absence of a structured methodology that is supportiveMaintained
B14In the workplace, resistance to BIM adoption remains strongMaintained
Table
Table 4. Background information of respondents showing category, classification, frequency and percentage.
Table 4. Background information of respondents showing category, classification, frequency and percentage.
CategoryClassificationFrequency%
ProfessionArchitect127.23%
Quantity Surveyor148.43%
Civil Engineer9959.64%
M&E Engineer74.22%
Project Manager3018.07%
Other42.41%
OrganizationContractor8048.19%
Consultant7645.78%
Client106.02%
Experience in the Malaysian Construction Industry0–5 Years3923.49%
5–10 Years5633.73%
1–15 Years5231.33%
15–20 Years116.63%
Over 20 Years84.82%
Experience in Small Construction ProjectsYes16599.40%
No10.60%
Table
Table 5. Reliability (Cronbach Alpha Test) and normality (Shapiro–Wilk Test) results.
Table 5. Reliability (Cronbach Alpha Test) and normality (Shapiro–Wilk Test) results.
CodeCronbach’s AlphaShapiro–Wilk Test
Statisticdfp Value
B010.8410.872 166 0.000
B020.8150.864 166 0.000
B030.8150.838 166 0.000
B040.8150.867 166 0.000
B050.8180.861 166 0.000
B060.8170.849 166 0.000
B070.8210.849 166 0.000
B080.8180.846 166 0.000
B090.8190.835 166 0.000
B100.8250.847 166 0.000
B110.8200.863 166 0.000
B120.8160.861 166 0.000
B130.8210.847 166 0.000
B140.8220.850 166 0.000
B150.8200.847 166 0.000
B160.8170.831 166 0.000
B170.8210.857 166 0.000
B180.8240.840 166 0.000
B190.8230.836 166 0.000
B200.8170.842 166 0.000
B210.8200.850 166 0.000
Note: p value of Shapiro–Wilk test is significant at the level of 0.05.
Table
Table 6. Mean score ranking of barriers to BIM in small construction projects.
Table 6. Mean score ranking of barriers to BIM in small construction projects.
CodeMeanSDRankMedianp Value 1p Value 2p Value 3p Value 4
B013.231.452143.000.0520.5040.1390.196
B023.201.470183.000.1660.014 *0.025 *0.856
B033.311.54554.000.004 *0.011 *0.000 *0.414
B043.281.43494.000.103 *0.4590.0360.143
B053.191.493193.000.3300.124 *0.0860.189
B063.421.46224.000.044 *0.021 *0.023 *0.838
B073.281.51294.000.3760.4530.020 *0.772
B083.301.51174.000.0760.1210.5130.403
B093.391.51244.000.040 *0.000 *0.4600.143
B103.281.51294.000.8760.033 *0.006 *0.427
B113.211.476173.000.0650.4970.1220.856
B123.051.509213.000.0540.0740.4650.172
B133.251.531133.000.1010.1920.0930.148
B143.251.508124.000.3050.1160.2990.732
B153.161.545203.000.028 *0.0640.002 *0.195
B163.401.52934.000.024 *0.026 *0.4980.872
B173.221.507153.500.006 *0.9990.039*0.806
B183.221.543154.000.4090.4740.1750.472
B193.491.46414.000.020 *0.5510.0650.131
B203.301.52774.000.1280.0910.040 *0.723
B213.311.50464.000.002 *0.4360.012 *0.397
1p value of Kruskal–Wallis test for intergroup comparison of respondents of different professions. 2 p value of Kruskal–Wallis test for intergroup comparison of respondents of different organizations. 3 p value of Kruskal–Wallis test for intergroup comparison of respondents of different experiences in the construction industry. 4 p value of Kruskal–Wallis test for intergroup comparison of respondents of experience in small projects. * p value of the corresponding test is significant at the level of 0.05.
Table
Table 7. KMO and Bartlett’s test results.
Table 7. KMO and Bartlett’s test results.
Kaiser–Meyer–Olkin Measure of Sampling Adequacy0.853
Bartlett’s Test of SphericityApprox. Chi-Square853.740
df210
Sig.0.000
Table
Table 8. Factor loadings indicating 5 components based on PCA with varimax rotation.
Table 8. Factor loadings indicating 5 components based on PCA with varimax rotation.
Barriers12345
B150.704
B140.652
B60.640
B30.571
B120.506
B11 0.674
B7 0.627
B2 0.588
B21 0.542
B13 0.513
B19 0.466
B4 0.406
B8 0.375
B18 0.754
B20 0.562
B16 0.471
B10 0.714
B9 0.493
B5 0.362
B17 0.336
B1 0.897
Eigen Values5.8831.2691.2281.1901.115
% of Variance28.0156.0415.8475.6655.311
Cumulative Variance %28.01534.05539.90345.56750.878
Table
Table 9. Mean score ranking of BIM barriers and distribution.
Table 9. Mean score ranking of BIM barriers and distribution.
SubgroupCodeBarriersMeanSubgroup MeanSubgroup Rank
Behavioral BarriersB15The lack of demand for or insistence on BIM from customers3.163.244
B14In the workplace, resistance to BIM adoption remains strong
3.25
B6Aversion to adopting BIM3.42
B3Aside from the construction team leader, no other team members need BIM3.31
B12Neither a simple nor universal strategy for BIM Usage exists3.05
Technical Adoption BarriersB11Absence of a structured methodology that is supportive3.213.292
B7Impractical theoretical evidence from research
3.28
B2No facilitation and training center for BIM
3.20
B21Poor BIM ability to integrate with project operations3.31
B13Lack of legal regulations3.25
B19High cost of BIM 3.49
B4Inadequate working processes and quality control standards3.28
B8Lack of awareness about the benefits of BIM3.30
Management BarriersB18Impact of COVID-19 on small construction projects3.223.311
B20The integration of BIM will change present levels of efficiency.3.30
B16High diversity of workforce in projects3.40
Implementation BarriersB10Possibility of delays in construction3.283.273
B9No financial support for small construction projects3.39
B5Insufficient teamwork from upper management3.19
B17Too many complexities in design produced by BIM3.22
Digital Education BarrierB1Lack of digital education and training3.233.235
Table
Table 10. Validity and reliability of CBBs showing acceptable statistics for all constructs.
Table 10. Validity and reliability of CBBs showing acceptable statistics for all constructs.
ConstructsCRAVEMSVMaxR(H)BBTABIMBMB
BB0.8770.5890.2300.8790.767
TAB0.8380.5000.2280.8410.4780.681
IMB0.7150.5570.2300.7150.4800.4480.746
MB0.7770.5390.1710.7860.3820.4130.3060.734
Table
Table 11. Goodness of fit (GOF) for the measurement model.
Table 11. Goodness of fit (GOF) for the measurement model.
IndexAcceptanceAttained
RMSEA<0.080.47
GFI>0.900.925
CFI>0.900.966
TLI>0.900.958
Cmin/df<2, 31.447
ChiSqp > 0.05, p > 0.01151.12
Table
Table 12. Goodness of fit (GOF) for the structural model.
Table 12. Goodness of fit (GOF) for the structural model.
IndexAcceptanceAttained
RMSEA<0.080.47
GFI>0.900.925
CFI>0.900.966
TLI>0.900.958
Cmin/df<2, 31.447
ChiSqp > 0.05, p > 0.01140.350
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Waqar, A.; Qureshi, A.H.; Alaloul, W.S. Barriers to Building Information Modeling (BIM) Deployment in Small Construction Projects: Malaysian Construction Industry. Sustainability 2023, 15, 2477. https://doi.org/10.3390/su15032477

AMA Style

Waqar A, Qureshi AH, Alaloul WS. Barriers to Building Information Modeling (BIM) Deployment in Small Construction Projects: Malaysian Construction Industry. Sustainability. 2023; 15(3):2477. https://doi.org/10.3390/su15032477

Chicago/Turabian Style

Waqar, Ahsan, Abdul Hannan Qureshi, and Wesam Salah Alaloul. 2023. "Barriers to Building Information Modeling (BIM) Deployment in Small Construction Projects: Malaysian Construction Industry" Sustainability 15, no. 3: 2477. https://doi.org/10.3390/su15032477

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