Title: Factors Affecting Successful BIM Implementation from a Developing Country’s Perspective

Authors:

Present Address:

AbdullahAlShanto1,2✉Emailabdullahalshanto@masroors.com

KaziAbuManjur2Emailkamanjur@duet.ac.bd

AsifFaisalAnanta3Emailasiffaisalananta89@gmail.com

SishirDas3Emailsishir05.ce@gmail.com

MiskatRahman3Emailmiskatrahman47@gmail.com

Masroors PLLCArizonaUnited States

2Department of Civil EngineeringDhaka University of Engineering & TechnologyDhakaBangladesh

3Department of Civil Engineering, Mymensingh Engineering CollegeUniversity of DhakaMymensinghBangladesh

Abdullah Al Shanto^a,b,*, Kazi Abu Manjur^b, Asif Faisal Ananta^c, Sishir Das^c, Miskat Rahman^c

Affiliation:

^a Masroors PLLC, Arizona, United States;

^b Department of Civil Engineering, Dhaka University of Engineering & Technology, Dhaka, Bangladesh;

^c Department of Civil Engineering, Mymensingh Engineering College, University of Dhaka, Mymensingh, Bangladesh;

^* Corresponding Author: Abdullah Al Shanto;

Email: abdullahalshanto@masroors.com

Emails of all authors:

Abdullah Al Shanto; Email: abdullahalshanto@masroors.com

Kazi Abu Manjur; Email: kamanjur@duet.ac.bd

Asif Faisal Ananta; Email: asiffaisalananta89@gmail.com

Sishir Das; Email: sishir05.ce@gmail.com

Miskat Rahman; Email: miskatrahman47@gmail.com

Title: Factors Affecting Successful BIM Implementation from a Developing Country’s Perspective

Abstract

Building Information Modeling (BIM) is a transformative paradigm in the global Architecture, Engineering, Construction, and Operations (AECO) sector, yet its adoption in developing nations like Bangladesh remains nascent. Therefore, this research provides a comprehensive analysis of the critical factors, benefits, and challenges of shaping BIM implementation from the perspective of a developing country, Bangladesh. By employing a mixed-methods approach, integrating findings from a survey of 268 AECO stakeholders (students, educators, and professionals) with a 4D simulation case study of a multi-story commercial building. The survey reveals a critical gap between awareness and competency; while 77.2% of respondents are familiar with BIM, only 34.4% have received professional training. An exploratory factor analysis confirmed four predefined sections, classified and identified 32 crucial factors. Then, a Relative Importance Index (RII) identified university-industry collaborations for BIM programs and government support and enforcement as the most critical drivers for adoption. Other vital factors indicated acknowledgment of BIM’s efficiency, inadequate industry knowledge, a lack of promotion, seminars, and the establishment of training courses. An independent samples t-test exposed a substantial perceptual gap between the optimistic expectations of students and the pragmatic views of professionals. The 4D simulation case study validated BIM's practical benefits, demonstrating a 13.5% reduction in project completion time and revealing a strong potential for substantial cost savings through the early detection of 355 design clashes. This study contributes a robust, empirically supported roadmap for policymakers and industry leaders by offering specific, actionable recommendations to accelerate BIM adoption to enhance construction sector productivity.

Keywords:

Building Information Modeling

Construction Industry

Developing Countries

4D Simulation

BIM Education

1. Introduction

The construction industry is a significant contributor to the global GDP, approximately 13% and generates around $10 trillion annually, and is projected to reach $15.5 trillion by 2030 [1–3]. However, it often fails to deliver value for money due to the uniqueness, fragmentation, and the different phases of each project [4, 5]. This leads to poor communication, scheduling issues, and a complex collaborative environment, resulting in rework and increased costs [6, 7]. To facilitate effective collaboration, knowledge sharing, and sound relationships among the stakeholders, BIM is emerging as a promising tool by providing and meeting the necessary demands and through cost savings, time management, better design standards, and improved teamwork [8–10].

In 1970, Eastman first pioneered the BIM concept by introducing the Building Description System (BDS) [11]. The first building modeling software programs emerged in the late 1970s and early 1980s [12]. However, technological, especially hardware, limitations initially made BIM, but recent advancements have made it a cornerstone of the industry [13]. BIM is a process that generates, operates, and manages information throughout the entire construction lifecycle and is used for visualization, coordination, simulation, optimization, and drawing [14]. It is useful in planning phases for digital models and data management [15], aids in structural analysis [16], and provides a digital representation of all components to facilitate stakeholder collaboration [17, 18]. Moreover, BIM helps reconstruct damaged buildings and ensures information continuity [16]. Advanced BIM dimensions, 4D (scheduling or cost estimation), can address visual deficiencies and design understanding, time coordination, and cost estimation, enabling the reduction of delays and improving project performance, decision-making, and timely delivery [19, 20]. Therefore, integrating BIM in various types of construction can improve resource management, optimization processes, and overcome challenges in traditional 2D methods [21].

Leading countries on BIM implementation (the USA, the UK, Australia), and other countries like China, have benefited from adopting it [22, 23]. In Europe, BIM use is rising due to mandatory requirements, though concerns remain regarding its implementation during the execution phase [24]. In contrast, BIM adoption in developing regions like Bangladesh has been slower, with the first research publications emerging only in 2018 [25], whereas China, India, and Malaysia had focused studies before 2013 [26]. BIM implementation faces barriers in developing economies due to limited investment in technology and reluctance to abandon traditional practices [27, 28]. Mostly, the industry hesitates to adopt BIM due to the high initial software and hardware costs, lack of knowledge, profitability concerns, and the steep learning curve [29, 30]. Early-stage adopters, such as those in Bangladesh, require effective awareness, a conducive learning environment, and an understanding of this technology [31]. Therefore, to advance BIM implementation in these regions, identifying the root factors hindering adoption is important [32]. Despite proven benefits, the developing countries are lagging in BIM adoption, partly due to the gap between education and skilled industry professionals [33, 34]. Higher education institutions face challenges in educating future professionals about BIM due to a lack of strategies [35]. To equip students with the necessary skills, countries like Bangladesh must integrate BIM into higher education, and this could be achieved by redesigning curricula and ensuring BIM education for future architects and engineers [31, 36].

Since BIM adoption and research in updated construction technologies are thriving in developed regions, the developing regions must come forward and take steps to facilitate faster adoption. Bangladesh is one such country that is left behind in BIM adoption. While numerous studies are available on identified barriers to BIM adoption in developed regions, few have employed a mixed-methods approach to both quantify the perceptions of a wide range of stakeholders and simultaneously validate these perceptions with a technical case study, in a nascent market like Bangladesh. To address these limitations and foster digital construction transformation, this study explores the introduction of BIM's challenges, potential, and benefits in Bangladesh's AECO industry through a systematic survey and a 4D case study. It aims to identify gaps, provide insights for policymakers and professionals, and make recommendations to improve BIM adoption in the country’s respective sectors.

2. Methodology

This study employed a convergent mixed-methods approach by incorporating a quantitative survey and a 4D case study to provide a comprehensive analysis of BIM implementation factors. The research process is illustrated in Fig. 1.

Fig. 1

Research methodology flowchart.

Figure 1 Descriptive Captions

A flowchart depicting the research methodology. It starts with Literature Research, including previous research reviews and problem identification. The next phase, Survey Framework Development, involves setting up survey questions, distributing forms, and refining responses. Simultaneously, Case-study Framework Development focuses on gathering construction documents and identifying methods for 4D simulation. Next is Survey Data Analysis, which includes reliability tests, exploratory factors, and T-tests. 4D Simulation covers structural, architectural, and MEP modeling, importing files into Navisworks, clash detection, and timeline simulation. The final stage is the Conclusion and Future Directions.

2.1 Survey Framework and Parameters

The survey participants considered were university teachers, students, and professionals. Firstly, it consisted of closed-ended questions for quicker processing and analysis [37] and to capture respondents' backgrounds, experiences, and opinions on BIM and its implementation's challenges, acceptance, and benefits. The survey questions were developed based on direct observations and literature reviews, and categorized as follows:

Sections 1–3: To capture the respondent's background.

Sections 4–5: Experiences and opinions about BIM.

Sections 6–9: Acceptance, challenges, benefits, and adoption of BIM

The initial questions mostly dealt with respondents' background and familiarity with BIM. Then the questionnaires, were to collect information about opinions or behaviors, are used in various fields, and can be administered by mail, in person, online, or by phone [38]. We used a 5-point Likert scale, which is commonly used in questionnaires, to gauge participants' preferences or agreement levels with statements [39, 40]. The questionnaire is designed and developed to align with the objective of an extensive literature review (Table 1).

Table 1
Identified factors and questionnaire design
Sec.	Code	Question	Description	Ref.
AC	AC-1	BIM could speed up tasks on the job	Customizes tasks for project needs, resulting in faster outcomes.	[41, 42]
	AC-2	BIM improves job performance	BIM skills enhance job performance and efficiency.	[43]
	AC-3	BIM increases one’s job productivity	It boosts productivity and saves time on construction sites.	[43, 44]
	AC-4	BIM enhances one’s effectiveness	Improves cost management, collaboration, and quality results.	[45, 46]
	AC-5	BIM simplifies tasks in the job field	Coordinates models, resolves conflicts, and supports project management.	[47, 48]
	AC-6	Learning and operating the BIM software is easy	Easy with problem-solving in a flipped classroom and targeted vignettes.	[49, 50]
	AC-7	Interaction with BIM has been clear and understandable	Lack of understanding and clear design standards hinders digitalization in the AEC industry.	[51, 52]
	AC-8	Easy to become BIM-skilled	Effective BIM implementation requires essential training and digital skills.	[53]
CH	CH-1	Inadequate BIM knowledge	Low BIM knowledge among practitioners and students hinders broader adoption.	[54, 55]
	CH-2	Insufficient training in BIM software usage	Shortage of trained personnel and few training programs hinder adoption.	[54, 56]
	CH-3	Software and hardware are too expensive.	The initial high software cost presents a hurdle to BIM adoption, especially for SME’s	[56, 57]
	CH-4	The current CAD software needs replacement	Current CAD systems often need more support for design reasoning and require customization.	[58, 59]
	CH-5	BIM isn't necessary for the project/team in the present practice	Some teams do not see the need for BIM, preferring traditional methods over interoperability.	[60, 61]
	CH-6	Resistance to change from traditional methods	BIM implementation faces resistance due to traditional construction practices.	[62]
	CH-7	No Return on Investment (ROI) data for BIM	There is no standard method for calculating ROI, schedules, and productivity using BIM.	[63]
	CH-8	Lack of standardized procedures	Interoperable BIM libraries and standardized practices are not essential and need improvement.	[64, 65]
BE	BE-1	Minimizes conflicts during construction	BIM Minimizes uncertainties, errors, and delays, and also improves collaboration.	[66, 67]
	BE-2	BIM ensures higher-performing structures	Improves design quality, safety, and efficiency in construction projects.	[68]
	BE-3	BIM enables a faster review and approval process	BIM identifies and rectifies design issues quickly, improving processes.	[69]
	BE-4	BIM supports advanced prefabrication option	Integrates with prefabrication for faster error visualization and smoother workflows.	[70]
	BE-5	BIM reduces the project risk	BIM improves quality and reduces risks in construction projects.	[71, 72]
	BE-6	BIM enhances project quality	BIM helps quality management by improving the efficiency of labor, materials, and equipment throughout the lifecycle.	[14, 73]
	BE-7	BIM could save costs through waste reduction	Reduces material waste and improves waste estimation while designing.	[9, 74]
	BE-8	It gives better visualization and design control	Streamlines construction by reducing waste and improving efficiency through visualization and coordination.	[14, 41]
	BE-9	Cloud-based capability	Cloud-based collaboration and data storage align with industry trends.	[75]
AD	AD-1	Government Support and enforcement for BIM	Governments mandate BIM adoption for public projects to enhance implementation.	[76, 77]
	AD-2	Clients willing to invest in BIM	Client-mandated BIM and independent consultants ensure successful implementation.	[78]
	AD-3	The need for client demand for BIM	The construction industry is seeing rising client demand for BIM, with Malaysia aiming for 50% implementation by 2025.	[79]
	AD-4	Create a standardized work procedure for BIM	Interoperable BIM libraries ensure information sharing and coherence.	[65]
	AD-5	BIM training courses establishment	Establishing BIM training courses, management systems, lab exercises, and updated curricula is necessary.	[36, 80]
	AD-6	BIM awareness, promotion, and seminar	Awareness campaigns and advanced training programs to prepare professionals.	[79, 81]
	AD-7	Hire a qualified BIM expert	Transitioning to BIM faces challenges due to resistance and limited trained professionals.	[82, 83]
	AD-8	University collaborations for BIM programs	University and industry collaboration helps bridge gaps in outdated curricula and instructor expertise.	[36, 84]
Acceptance = AC, Challenges = CH, Benefits = BE, and Adoption = AD
2.2 Data Collection
2.2.1 Survey Data Collection

Data collection started with distributing the survey and reaching 6248 potential respondents in the country's AECO and education sector. More details are given in Table 2. To mitigate potential biases, we anonymized the survey to encourage honest responses and then conducted a pilot test to identify and correct leading questions or ambiguities. Control questions were included to detect inconsistencies and improve the clarity and neutrality. Participants were informed in the invitation letter sent to them and at the introduction of the online survey about the research objectives and use of their responses (academic purposes only). By voluntarily completing the survey, participants provided their implied consent to participate in the study. No personal or sensitive data was collected to ensure participant anonymity and confidentiality.

Table 2
Summary of survey data collection method and target groups
Category	Details
Method	Survey via Google Forms
Groups	Teachers, Industry Professionals, Students
Teachers	Details of 1,022 teachers collected from institutional websites
Professionals	A list of 3,309 AECO professionals is prepared via the employment or organization's websites.
Students	2,187 students were reached via campus representatives or their teachers. (36 institutions dominant institutions in the AECO industry, were considered)
Disciplines	Civil Engineering (CE), Architecture, Building Engineering and Construction Management (BECM), and Urban and Regional Planning (URP)

2.2.2 Case Study Data Collection

The case study is considered the [Location/Project name removed for blind review] (Fig. 2 and Table 3), a recently completed eleven-story commercial building, constructed using the traditional 2D methods, which is a common practice in Bangladesh. Permission was secured from the authority to access all architectural, structural, and MEP drawings, as well as the plan and actual schedules. Due to confidentiality agreements, this study did not include direct cost data and financial proprietary information. The focus was on analyzing schedule-related aspects, rework avoidance, and improved efficiency.

Table 3. Overview of the case study

Category	Details
Building details	Eleven stories tall with a semi-basement and a basement. Gross area of 13,500 square feet.
Contractor	Dhali Construction, Brothers and Brothers Construction
Architect	[Name removed for blind review]
Construction Cost	43 Crore Bangladeshi Taka
Total Time	4 months for the design and 48 months (delayed) for construction

Figure 2. [Project name/picture removed or blurred for blind review]

Figure 2 Descriptive Captions

Photograph of a tall, modern, white building surrounded by palm trees. The building has a uniform grid of windows and stands out against a bright, partly cloudy sky. The foreground includes a driveway leading to the building, with trees framing the structure from both sides.

2.3 Survey Data Analysis

First, the respondents' demographics were analyzed, and then the questionnaire data were analyzed [85]. Survey data analysis completed utilizing specialized software ensures efficiency and ease; SPSS 27 is used here for processing and analysis [86]. Initial tests estimated error levels and checked reliability using Cronbach's alpha to assess the internal consistency of the questionnaire, and high values will confirm that Likert scale statements reliably measured the intended constructs [87, 88]. The α values greater than 0.9 indicate excellent reliability, 0.8–0.89 good reliability, 0.7–0.79 acceptable reliability, 0.6–0.69 questionable reliability, 0.5–0.59 poor reliability, and values less than 0.5 are considered unacceptable [89].

Exploratory Factor Analysis (EFA) was employed to identify the underlying dimensions of the questionnaire. Firstly, Bartlett's Test of Sphericity (p < .05) and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (> 0.60) confirmed the data's suitability for factor analysis. [90–92]. Principal Component Analysis (PCA) and varimax rotation were used to distribute the workload among variables better. To follow the commonly accepted norm in EFA, the cutoff score for the factor loadings is taken as 0.5 [92].

Item-level descriptive statistics are crucial for assessing reliability and validity in questionnaire surveys [93]. Also, the Relative Importance Index (RII) is beneficial for identifying critical criteria based on responses, uncovering preferences, and detecting unobserved issues [94, 95]. RII values were interpreted as follows: values between 0.8 and 1.0 indicate high importance, 0.6 to 0.8 high-medium importance, 0.4 to 0.6 medium importance, 0.2 to 0.4 medium-low importance, and 0.0 to 0.2 low importance [96].

Relative Importance Index, RII =

$\:\frac{\sum\:W}{A\:\times\:N}$

[95] (1)

Where, W = Weightage assigned to each variable (range 1 to 5); A = Highest score obtainable is 5; N = Total number of respondents

An independent samples t-test was performed at a significance level of α = .05 to compare perceptions of BIM implementation factors among different stakeholders (students, teachers, and industry professionals). The null hypothesis is rejected if the p-value is ≤ .05 [97], shows significant differences in perceptions. The developed hypothesis for the test is given below:

Null Hypothesis (H₀)

Based on the opinions of different groups, there is no significant difference in the perceptions (based on the group means) concerning the acceptance, challenges, benefits, and adoption of BIM (i.e., µ₁ = µ₂).

Alternate Hypothesis (H₁)

Based on the opinions of different groups, there is a significant difference in the perceptions (based on the group means) concerning the acceptance, challenges, benefits, and adoption of BIM (i.e., µ₁ ≠ µ₂).

2.4 4D Simulation Protocol

A comprehensive 4D simulation was conducted using Autodesk Revit 2024 and Navisworks Manage 2024.

The process adhered to the principles of the ISO 19650 series of standards and World Skills France's guidelines. Modeling started in Autodesk Revit with the Imperial Structure template, defining grids and levels, and then creating structural components (foundation slab, columns, beams, floor slabs, roofs, and stairs). Next, the architectural model was created using the Imperial Architectural Template. The structural model is linked, grids and levels are copied, and all the architectural elements are placed. The MEP model was then integrated, and mechanical, electrical, and plumbing systems were accurately placed. Once completed, all models were purged and exported to Navisworks, where they were federated into a single coordinated model. A clash detection analysis was run with a 30 mm tolerance to identify interferences (Table 4). Then clashes were identified, and scheduling was managed. The Time-Liner module was activated for simulation, visually linking construction activities to the 3D model over time. The modeling details, naming, folder structures, and approval workflows were followed and detailed in Table 5.

Table 4
Sets for clash detection and tolerance.
Analysis	Selection A	Selection B	Tolerance (mm)	Type
Beams VS Ducts	Beams	HVAC ducts	30	Clearance
Beams VS Electrical	Beams	Cable Trays	30	Hard
Ceiling VS Ducts	Ceilings	HVAC ducts	30	Clearance
Ceiling VS Electrical	Ceilings	Cable Trays	30	Hard

Table 5
BIM tools that were used for the case study.
Segments	Objectives	Software Used
3D modeling	Creating a 3D model using available data [98]	Autodesk Revit 2024
Structural Modeling	Creating a 3D model using available data [98]	Autodesk Revit 2024
MEP modeling	Mechanical, electrical, and plumbing models [99]	Autodesk Revit 2024
Clash detection analysis	Before construction, need to check clashes in the design [66]	Autodesk Revit 2024 and Navisworks Manage 2024
Scheduling (4D Model)	According to the schedule, time estimation, and project visualization [100]	Autodesk Revit 2024 and Navisworks Manage 2024

3. Results

3.1 Survey Findings

3.1.1 Demographics and Experience

The survey sample was composed of students (55%), industry professionals (33%), and educators (12%), with a majority from Civil Engineering (80%). Their education level and backgrounds are detailed in Fig. 3. The designations or current status at their job of teachers and industry professionals are given in Table 6.

Table 6
Designation of respondents (By percentage of faculty or professionals)
Teacher Designation	Industry Professionals Designation
Professor 21.7%	CEO 3.3%
Associate Professor 17.4%	Chief Engineer 11.1%
Assistant Professor 34.8%	Additional Chief Engineer 1.1%
Lecturer 21.7%	Project Director 6.7%
	Additional Project Director
	Superintending Engineer 1.1%
	Executive Engineer 12.2%
	Project Manager 7.8%
	Sub-Divisional Engineer 1.1%
	Assistant Engineer 27.8%
	Design Engineer 13.3%
	Junior Engineer 10%
	Project Engineer 1.1%
	Associate Architect 1.1%

Fig. 3

Demographics of the respondents

Figure 3 Descriptive Captions

Bar chart representing the distribution of participants based on background, education level, and affiliation. The x-axis shows percentages from 0% to 80%, and the y-axis lists the categories. In the 'Background' section, most participants are students (~ 60%), with smaller groups being government employees, non-government employees, self-employed, entrepreneurs, and teachers. In the 'Education' section, undergraduates account for the majority (~ 70%), followed by graduates, with very few PhD participants. The 'Department' section shows that most participants are from Civil Engineering (~ 75%), with smaller groups from Architecture, Building Engineering & Construction Management, and Urban & Regional Planning.

The most striking finding is a profound awareness-competency gap. As shown in Fig. 4, a significant majority of respondents (77.2%) are aware of BIM, yet only a small fraction (34.4%) have received any professional training. This indicates that while BIM as a concept is well-known, the practical skills required to implement it are scarce. Among them, 38.4% are very familiar, 34.3% are somewhat familiar, and 26.9% are unfamiliar with the BIM concept in Fig. 4 (b).

Fig. 4

(a) Ever heard about BIM, (b) Familiarity with the BIM concept, and (c) Ever received professional BIM training

Figure 4 Descriptive Captions: Three pie charts illustrating survey responses related to BIM. Chart (a) shows that 77.2% of respondents have heard about BIM, while 22.8% have not. Chart (b) presents familiarity with BIM: 38.4% are very familiar, 34.3% are somewhat familiar, and 26.9% are not familiar at all. Chart (c) indicates that 65.6% of respondents have not received professional BIM training, while 34.4% have.

This disconnect is further substantiated by software usage patterns (Fig. 5), which show the persistent dominance of traditional 2D software (AutoCAD, 88.1%) over BIM-authoring platforms like Revit (37.7%), while ArchiCAD and Civil 3D are used by less than 10% and they are not yet standard in most university curricula. This indicates that while BIM is a familiar concept, the industry's operational workflows remain anchored in legacy methods, so there is a critical need for targeted, hands-on training to translate conceptual awareness into practical competency. Results show diverse applications of CAD software within institutions for both project and educational purposes (Fig. 6).

The majority of respondents use CAD for later directions in structural analysis (65.5%), planning and scheduling (52.1%), and visualization (36%). Other notable uses include estimating (31%), site planning (41.4%), cost management (30.3%), facility management (21.8%), clash detection (18.4%), and site safety (22.6%). So, these tasks can be easily facilitated by BIM integration; however, they are doing the cumbersome task with 2D CAD. Regarding the learning method, 70.1% favor seminars or workshops for BIM training, 64.6% prefer self-learning, and 32.1% choose in-house training. The readiness and necessity of undergraduates to learn and apply BIM concepts at both institutional and professional levels are presented in Fig. 7.

Fig. 5

A combination of the CAD software used by the respondents

Figure 5 Descriptive Captions

Bar chart showing the usage percentages of various software tools. AutoCAD is the most used (88.1%), followed by E-Tables (44%), Revit (37.7%), and SketchUp (28.7%). Other tools include STAAD Pro (14.6%), 3Ds Max (12.7%), Autodesk Navisworks (12.3%), ArchiCAD (9%), Civil 3D (7.5%), and a few others with low usage, including Rhino, Sweet Home 3D, and Rhinoceros (each below 1%).

Fig. 6

Purpose of CAD software used in institutions

Figure 6 Descriptive Captions

Bar chart showing the application of various tasks in a survey. Structural analysis is the most common task (65.5%), followed by planning and scheduling (52.1%), visualization (36%), cost management (30.3%), estimating (31%), and site planning (41.4%). Other tasks include facility management (21.8%), site safety (22.6%), clash detection (18.4%), and a category marked as N/A (24.5%).

Fig. 7

Readiness of undergraduates to learn and apply BIM (a) When institutions should implement BIM; (b) Necessity of BIM knowledge to withstand IR 4.0; and (c) BIM knowledge is necessary to withstand the I.R. 4.0. era.

Figure 7 Descriptive Captions

Three pie charts showing survey results for the readiness and necessity of undergraduates to learn and apply BIM. In chart (a), 35% of respondents consider themselves 'Very prepared,' 43.5% are 'Somewhat prepared,' while 21.5% feel 'Not prepared' to learn and apply BIM. Chart (b) displays the timeline for BIM implementation, with 36% suggesting it should be implemented next year, 23% within the next three years, and 41% over the next five years. The last chart (c) shows that 95.5% believe BIM knowledge is essential to withstand IR 4.0.

3.1.2 Reliability and Validation

The results of reliability and factor analysis are detailed in Table 7. The reliability of “per section” and “if an item was deleted” using Cronbach's Alpha was excellent and exceeded 0.9. The EFA was conducted on each section first, and no underlying factors existed. After this, the test was performed on the 33 items, and four underlying factors were identified, accounting for 78.022% of the total variance. The data were suitable for factor analysis, confirmed by Bartlett's test of sphericity (χ2 = 10599.525, df = 528, p < 0.001), and a KMO measure of sampling adequacy was 0.966. The item CH-2 was removed in future analyses due to its low loading. A key analytical decision involved the elimination of Factor 4, which was deemed theoretically redundant and had minimal contribution to the explained variance (1.520%). The items that loaded onto this factor were thematically related to the benefits of BIM but did not form a distinct, coherent construct separate from the broader "Benefits and Adoption" factor (Factor 1). For instance, item BE-2 loaded significantly on both factors. To maintain theoretical clarity and statistical robustness the items from Factor 4 were reassigned to Factor 1, which was the most theoretically appropriate destination. This process of refining the factor structure, ultimately supported the robustness of the original, theoretically derived sections of the survey.

Table 7
Reliability and Exploratory Factor Analysis
Section	Code	CA		EFA Pattern coefficients (For 33 items)
Section	Code	CA^a	CA^b	Factor 1	Factor 2	Factor 3	Factor 4
AC	AC-1	0.953	0.961 (N of items: 8)		0.787
	AC-2	0.955			0.817
	AC-3	0.953			0.804
	AC-4	0.955			0.804
	AC-5	0.954			0.828
	AC-6	0.959			0.700
	AC-7	0.957			0.742
	AC-8	0.957			0.724
CH	CH-1	0.926	0.932 (N of items: 8)			0.517
	CH-2	0.927				0.466
	CH-3	0.925				0.707
	CH-4	0.930				0.668
	CH-5	0.920				0.787
	CH-6	0.923				0.816
	CH-7	0.919				0.806
	CH-8	0.917				0.789
BE	BE-1	0.963	0.968 (N of items: 9)	0.733
	BE-2	0.970		0.522			0.624
	BE-3	0.963		0.744
	BE-4	0.964		0.726
	BE-5	0.962		0.782
	BE-6	0.963		0.688
	BE-7	0.963		0.744
	BE-8	0.963		0.736
	BE-9	0.963		0.733
AD	AD-1	0.953	0.959 (N of items: 8)	0.804
	AD-2	0.955		0.757
	AD-3	0.955		0.725
	AD-4	0.953		0.750
	AD-5	0.951		0.804
	AD-6	0.954		0.813
	AD-7	0.953		0.784
	AD-8	0.954		0.789
CA^a: Cronbach Alpha if item deleted section-wise; CA^b: Cronbach Alpha per section; Factor 1: Benefits and Adoption; Factor 2: Acceptance; Factor 3: Challenges; Factor 4: Null

3.1.3 Ranking of BIM Implementation Factors

The Relative Importance Index (RII) analysis pinpointed the key levers and obstacles for BIM implementation (Table 8). The results reveal institutional support ranking with the highest significance. The overall and adoption section top-ranked factor was "University collaborations for BIM programs" (AD-8, RII = 0.801), followed closely by "Government Support and enforcement for BIM" (AD-1, RII = 0.785). This underscores a strong belief among all stakeholders that a coordinated push from both educational institutions (bottom-up) and government bodies (top-down) is essential. The perception “BIM improves job performance” ranks third overall and first in the acceptance section (AC-2, RII = 0.785). “BIM training courses establishment” (AD-5, RII = 0.774) and “BIM awareness, promotion, and seminar” (AD-6, RII = 0.774) both have tied with an overall rank 4 and a section rank 3, are crucial for successful implementation.

The most significant perceived challenge is "Inadequate BIM knowledge" (CH-1, RII = 0.772, overall rank 4), providing further statistical evidence for the awareness-competency gap. From a benefits perspective, "BIM reduces the project risk" (BE-5, RII = 0.772) was ranked as a top factor. These two, CH-1 and BE-5, and AC-1 (BIM could speed up tasks on the job), combinedly overall ranked fifth. Indicating that stakeholders view BIM not just as an efficiency tool but as a critical risk mitigation strategy, with a strong acknowledgement of the lack of knowledge. Other important influencing factors that show the positive perception of respondents towards BIM are, it could speed up tasks on the job and increase productivity. Additionally, respondents highly value better visualization, faster review and approval processes, support for advanced prefabrication, and clash-free construction as top benefits that could be achieved by implementing BIM. The other most significant technological barrier identified within the study was that software and hardware are too expensive, and resistance to change from traditional methods.

3.1.4 Perceptual Differences Among Stakeholders

The independent samples t-test revealed (Table 8) statistically significant and consistent perceptual differences between students and industry professionals, and some differences between students and teachers.

Table 8
Relative Importance Index Ranking, Standard Deviation, and T-test
Section	Code	SD	RII	Rank Section-wise	Rank Over-all	Independent t-test results at significance level 0.05, p-value
Section	Code	SD	RII	Rank Section-wise	Rank Over-all	St - Tc	Tc - IP	St - IP
AC	AC-1	1.139	0.772	2	5	0.036	0.183	0.000
	AC-2	1.109	0.780	1	3	0.258	0.4396	0.018
	AC-3	1.149	0.767	3	6	0.014	0.353	0.000
	AC-4	1.060	0.753	5	15	0.414	0.099	0.001
	AC-5	1.169	0.763	4	12	0.032	0.568	0.002
	AC-6	1.120	0.730	8	21	0.002	0.198	0.000
	AC-7	1.067	0.741	6	16	0.002	0.336	0.000
	AC-8	1.065	0.734	7	20	0.005	0.211	0.000
CH	CH-1	1.133	0.772	1	5	0.008	0.723	0.000
	CH-3	1.067	0.711	2	23	< 0.001	0.666	0.000
	CH-4	1.236	0.673	7	28	0.930	0.002	0.000
	CH-5	1.078	0.689	5	26	0.008	0.047	0.000
	CH-6	1.169	0.696	4	25	0.016	0.148	0.000
	CH-7	1.137	0.683	6	27	0.029	0.084	0.000
	CH-8	1.126	0.705	3	24	0.216	0.009	0.000
BE	BE-1	1.122	0.763	5	11	0.001	0.867	0.000
	BE-2	1.166	0.735	9	19	0.542	0.018	0.000
	BE-3	1.089	0.765	3	9	0.002	0.756	0.000
	BE-4	1.032	0.766	2	8	0.006	0.727	0.000
	BE-5	1.079	0.772	1	5	0.002	0.584	0.001
	BE-6	1.079	0.760	7	13	0.125	0.426	0.000
	BE-7	1.054	0.763	6	12	0.001	0.894	0.000
	BE-8	1.101	0.764	4	10	0.002	0.676	0.000
	BE-9	1.117	0.757	8	14	0.005	0.830	0.000
AD	AD-1	1.156	0.785	2	2	0.031	0.241	0.000
	AD-2	1.038	0.722	7	22	0.335	0.043	0.000
	AD-3	1.108	0.737	6	18	0.702	0.002	0.000
	AD-4	1.041	0.738	5	17	0.965	0.006	0.000
	AD-5	1.119	0.774	3	4	0.226	0.077	0.000
	AD-6	1.054	0.774	3	4	0.029	0.929	0.000
	AD-7	1.070	0.766	4	7	0.012	0.589	0.000
	AD-8	1.128	0.801	1	1	0.099	0.136	0.000

The mean difference is significant at the .05 level; the boldface indicates such values. Standard Deviation (SD), Student (St), Teacher (Tc), Industry Professional (IP)

The independent samples t-test showed one of the study's most crucial findings, with a statistically significant and consistent perceptual gap between students and industry professionals. Students consistently showed a more optimistic view, assigning significantly higher importance to BIM's benefits (e.g., AC-1: "speed up tasks,") and underestimating the difficulty of adoption (e.g., AC-8: "Easy to become BIM-skilled,"). In stark contrast, industry professionals placed significantly greater emphasis on the real-world barriers. They rated factors such as "Software and hardware are too expensive" (CH-3) and "Resistance to change from traditional methods" (CH-6) as far more critical challenges than students did.

Moreover, there were some differences between the perceptions of students and teachers (St-Tc). They mostly agreed on the adoption section factors.

Moreover, they also agreed on some of the factors of acceptance, challenges, and benefits, including AC-2, AC-4, CH-4, CH-8, BE-2, and BE-6. In stark contrast, the perceptions of educators and industry professionals were largely aligned, with very few significant differences. This pattern strongly suggests that the primary disconnect is not between academia and industry as a whole, but rather between the theoretical expectations of the next generation of professionals (students) and the pragmatic, experience-based realities faced by the current practitioners. It also suggests the necessity of specifically addressing strategies for each group's needs.

3.2 Findings from Case Study

3.2.1 Clash Detection

The federated 3D model served as a powerful diagnostic tool, revealing 355 major clashes between the interdisciplinary system that went undetected during the original 2D design process (Table 9). The majority of these (210) were clearance clashes between structural beams and HVAC ductwork. Each of these clashes represents a potential source of on-site rework, material waste, and schedule disruption that could be entirely avoidable through a BIM-driven, coordinated workflow. The Project Director also noted that additional clashes arose from a lack of coordination and design changes. Early detection and resolution of these clashes can significantly reduce unnecessary expenses. The absence of BIM implementation in this project contributed to these avoidable costs.

Table 9
Clash detection result
Analysis	Type	Clashes
Beams VS Ducts	Clearance	210
Beams VS Electrical	Hard	135
Ceiling VS Ducts	Clearance	9
Ceiling VS Electrical	Hard	1

Fig. 8

Clash detection in Autodesk Navisworks Manage; Clash Report Sample of Beams vs. Cables (a), Clash Report Sample of Ceilings vs. Ducts (b).

Figure 8 Descriptive Captions

Two images display clash detection reports in Autodesk Navisworks Manage. The left image shows a clash between beams and electrical cables in a 3D model, highlighting a green beam and red cable, with details like clash tolerance, element IDs, material, and clash points. The right image shows the clash report between ceilings and ducts where a green duct and red ceiling element are highlighted, with similar clash details, including dimensions, coordinates, and element properties.

3.3 4D Simulation

The project's 4D timeline is shown in Fig. 9. This timeline shows how the project has developed over time and whether it is on schedule. Figure 10 shows a sample of the different construction stages of our case study. The project is simulated according to the actual and planned schedule to show how the design change and lack of coordination affected the project delivery. It explains how the task should be conducted and opens a path for simultaneous construction phase coordination and implementation.

Fig. 9

4D schedule and Project Timeline (Planned vs Actual):

Figure 9 Descriptive Captions

A 4D construction schedule timeline in Autodesk Navisworks Manage, showing the project's construction stages over time. On the left, tasks are listed with statuses, planned and actual start/end dates, and corresponding elements attached to each task. A Gantt chart on the right shows the project timeline for various construction tasks from 2018 to 2021, with overlapping tasks visualized across quarters. Above the chart, a 3D model of the case study is displayed, which is linked to the tasks in the timeline.

Fig. 10

Different construction phases (Simulated)

Figure 10 Descriptive Captions

Simulation of the case study, showing different phases of the construction process. The first image illustrates the initial earth-excavation phase. The second image shows the base structure of the building, constructed up to the 5th floor, with visible columns and beams. In the third image, architectural walls are added, and the structure begins to take shape with reinforced walls highlighted in green. The final image displays the final project of the fully constructed high-rise building with finished architectural details.

3.4 Schedule Optimization and Enhanced Project Delivery

The case study project suffered significant delays, ultimately taking 48 months to complete. Interviews with the project director identified specific, high-impact events, such as a five-month delay for a fire safety system redesign and a six-month disruption following a change in the general contractor. The 4D simulation demonstrated that a BIM-based schedule, by enabling proactive problem-solving and optimizing the construction sequence, could have achieved a project completion time of about 41.5 months. This represents a 13.5% reduction in the total project timeline (Fig. 9). This quantifiable time saving, combined with the avoidance of rework from the 355 detected clashes, provides a powerful, evidence-based argument for the strong potential of BIM to deliver significant cost savings and improve project delivery certainty. Collectively, the survey results diagnose the human and organizational challenges of BIM adoption, while the case study quantifies the substantial, tangible rewards of overcoming them (Table 10).

Table 10
Project Delays and Solutions Provided by 4D BIM-Based Models
Problem	Delay	Cause	Solution with BIM
Drop Panel Installation	15 days	Lack of detailed information and visuals	Provided the necessary detailed information faster
Elevator Installation	Significant delay	Design mistake with share-wall height, resulting in a 2-foot shortfall	Prevented errors by providing accurate design details
Fire Safety and Alarm System Redesign	Coordination issues	Redesigning the system mid-project	Could update the design in real-time, which will help the contractor's body in the execution phase
Coordination between stakeholders	Five months	A 2D system lacks proper visualization, schedule, work progress, and planning.	Facilitates better coordination for future stakeholders
General Contractor Change	Six months	Change of the contractor mid-project	Improved planning and coordination with information on the construction project lifecycle to mitigate delay impacts
Design Changes and Clashes	Project delays	Design changes, clashes, and coordination issues	Allows early detection and resolution of clashes and design issues
Schedule Variance	More time than planned	Manual scheduling inefficiencies	Enhances accuracy and reduces delays with advanced scheduling tools (4D BIM)

4. Discussion

This study's mixed-methods design provides a robust understanding of adaptability, challenges, benefits, and implementation dynamics governing BIM adoption in Bangladesh. The findings clearly indicate that, like many developing nations, Bangladesh’s digital transformation is feeble, characterized by BIM's potential but constrained by significant structural barriers to its widespread implementation [28]. The survey data clearly delineate a profound gap between conceptual awareness (77.2%) and practical competency (34.4%) in BIM. This is not merely a lack of training but a systemic issue reflected in the continued dominance of legacy 2D workflows (88.1% AutoCAD usage). This gap is a primary barrier, as Inadequate BIM knowledge was ranked as the top challenge by stakeholders. The case study provides a clear illustration of the consequences: the 355 undetected clashes are a direct, physical manifestation of the coordination failures that arise from a workforce skilled in 2D methods but not in the collaborative, data-rich environment of BIM. Such findings align with literature showing that BIM’s clash detection capabilities reduce delays by identifying potential issues at early stages [71]. Wang et al. in 2023 further noted that these capabilities translate into tangible cost savings by optimizing resource allocation [67]. Additionally, BIM’s visualization and real-time update features improve project tracking [100]. So, it gave stakeholders a clear perspective of each phase, which is essential in Bangladesh. As shown in the case study that fragmented workflow and miscommunication are common in 2D CAD workflows. The literature strongly supports the argument that transitioning to BIM bridges the communication gaps [5, 14, 58, 59, 73, 75].

To analytically position these findings within a global context, a comparative analysis of BIM adoption in the emerging countries context was conducted. As shown in Table 11, certain barriers and drivers are nearly universal, while others are highly context-dependent. The analysis reveals the universal nature of certain barriers, particularly the high initial cost of technology, which is a consistent finding across nearly all BIM emerging regions.

Table 11
Clash detection result
Factor Category	Bangladesh (This Study)	Nigeria [101, 102]	Malaysia [103–105]	Turkey [103, 106]	India [107]
Barriers	Inadequate knowledge, high cost of software/hardware, and resistance to change	High Cost of software/hardware, lack of awareness, and lack of training/expertise	Lack of expertise, resistance from industry, and lack of government mandate (Private)	Lack of qualified staff, legal/contractual issues, and lack of leadership	High hardware/software costs, shortage of skills/expertise, and unclear benefit evaluation
Drivers	University-industry collaboration, government support, and improved job performance	Industry stakeholder commitment, capacity building, and organizational support	Government-led initiatives (Mandates), client demand, and desire for competitive advantage.	Availability of qualified staff, effective leadership, and availability of technology	Improved productivity, 3D visualization, and clash detection benefits
Primary Adoption Model	Emerging; bottom-up (education, training) & top-down (policy) mix needed	Emerging; focus on organizational commitment and capacity	Top-down; government-mandated for public projects	Maturing; Focus on human capital and leadership	Pre-adoption; Cost-benefit concerns are paramount

The preeminence of university-industry collaboration and government support resonates strongly with findings from other emerging markets. Malaysian study identifies top-down policy as the essential pillar for technology diffusion [103, 104]. While in Turkey, several universities are teaching BIM, but they emphasize the legal regulation, availability of qualified staff, and effective leadership [105] and research in Nigeria highlights "industry stakeholders' commitment" [102]. It likely reflects different stages of market maturity. Bangladesh, being at a very early stage, understandably prioritizes foundational capacity building through education. So, low BIM maturity and building foundational human capital through academia and government support are perceived as the most critical initial steps. As markets like Turkey may have moved past this initial phase and are now focused on the organizational and leadership capabilities required to scale adoption effectively. This positions the findings from Bangladesh not merely as a single-country case study but as a valuable contribution to a more nuanced global conversation on phased implementation strategies. Moreover, Hossain and Bin Zaman have also shown that countries with BIM-integrated curricula produce better-prepared graduates to meet industry demands [36]. This integration should include software skills, practical applications, and project-based learning to prepare students for real-world scenarios. Government intervention is also essential, as mandating BIM for public projects in countries like the UK has driven its adoption industry-wide [76]. Moreover, continuous professional development through seminars and workshops can help current professionals transition smoothly from traditional methods to BIM [79, 81].

The identified BIM adoption key barriers in Bangladesh are inadequate industry knowledge, high software costs, and resistance to shifting from traditional practices. These issues are also in those regions where digital construction processes are less familiar [62]. Construction professionals in developing countries often prefer traditional CAD systems due to familiarity and hesitation in investing in new technologies [30, 32]. Additionally, Hjelseth highlighted that a lack of supportive infrastructure could deter AECO professionals from adopting BIM [52]. In Bangladesh, the high initial costs of BIM software and hardware create obstacles, especially for small to medium enterprises, which are also seen in similar regions [29]. Literature suggests that government support through subsidies or incentives can mitigate financial challenges, and this strategy was successfully implemented in several East Asian countries [77].

To position the findings within a global context and generate a more significant theoretical contribution, this study proposes a novel Phased Model (Fig. 11) for emerging BIM Adoption regions in order to provide a more nuanced analytical framework. As per the survey findings, it is commendable that stakeholders in Bangladesh are aware of BIM, so the current scenario has moved more towards Phase 2. To successfully transit from phase 2 to 3, the government and educational policy makers must take necessary steps.

Fig. 11

A Phased Model of BIM Adoption

Furthermore, another notable contribution of this research is the empirical identification of the significant perceptual gap between students and industry professionals. Students are highly optimistic about its benefits, while Professionals, conversely, are acutely aware of the barriers, particularly the high initial costs and deep-seated cultural resistance to change, which are consistently cited as the most formidable impediments in developing economies [107]. This disconnect is not merely an academic observation. It underscores the urgent need to evolve beyond software proficiency to overcome and address the need for each group and the challenges of a lack of training, capital cost, resistance, and complexity of BIM Software [108]. Furthermore, the empirically identified "student-professional perception gap" offers a novel causal mechanism for the persistent barrier of "resistance to change," a key challenge in Phases 1 and 2. This is not simply institutional inertia; it is a socio-generational disconnect. When recent graduates, armed with theoretical optimism, advocate for BIM, their arguments may be dismissed by senior professionals who are more attuned to pragmatic concerns like the high cost of software and lack of ROI data, factors students rated as significantly less important. What appears as resistance to technology may be a rational rejection of a perceived unrealistic business case. This reframes the solution: it is not just about teaching students software, but about equipping them to build a compelling business case for BIM in the language of risk and finance.

This study underscores the necessity of a concerted effort to overcome the challenges facing BIM adoption in Bangladesh. While BIM’s potential to improve project efficiency, reduce costs, and enhance coordination is clear, widespread adoption requires addressing financial, educational, and infrastructural barriers. Through academic integration, government support, and professional development, Bangladesh’s AECO sector can make BIM a standard practice, elevating project outcomes and supporting sustainable development.

5. Conclusion and Recommendations

This study concludes that while the Bangladeshi AECO industry is aware of BIM's potential, its adoption is fundamentally constrained by a critical deficit in awareness-competency, a lack of institutional and governmental support, and significant, theoretically explainable perception differences between academic expectations and industry practice. The research establishes that a synergistic strategy focusing on top-down government policy and bottom-up university-industry partnerships is imperative to accelerate the nation's digital transformation in construction. The 4D simulation case study further substantiates this by demonstrating a potential 13.5% reduction in project completion time. Another contribution of this work is the phased model of BIM adoption, which offers an understanding and guiding digital transformation guide to the policy makers who are planning or thinking about BIM adoption in their specific region of interest.

Based on the comprehensive findings, the following specific and actionable recommendations are proposed:

National BIM Mandate and Roadmap: The Government of Bangladesh, through the Ministry of Housing and Public Works, should develop and publish a strategic roadmap for national BIM adoption. A key component should be a phased mandate requiring ISO 19650-compliant BIM on all public sector projects exceeding a certain amount in BDT threshold.

Curriculum Modernization: The University Grants Commission (UGC) of Bangladesh should work with universities to mandate the integration of dedicated BIM courses into all AECO-related undergraduate curricula within the upcoming academic years. Curricula must cover not only software proficiency but also collaborative workflows, information management principles, and the business case for digital project delivery to bridge the student-professional perception gap.

Industry-Academia Centers of Excellence: In response to the top-ranked driver identified in the RII analysis ("University collaborations for BIM programs"), universities should forge formal partnerships with leading AECO firms to establish "BIM Centers of Excellence." Universities should forge formal partnerships with leading AECO firms to establish "BIM Centers of Excellence." These centers would serve as hubs for joint research, professional development courses, and project-based internships for students, directly bridging the identified student-professional perception gap.

National BIM Competency Framework: A consortium led by the Institute of Engineers, Bangladesh (IEB) and the Institute of Architects Bangladesh (IAB) should establish a National BIM Competency Framework to formally define, assess, and certify professional BIM skills, thereby creating a clear and reliable standard for the industry.

Future research should conduct in-depth comparative case studies analyzing BIM implementation across public-sector megaprojects and private-sector commercial developments to yield valuable insights into context-specific challenges. They may also involve increased responses in the survey and may also include similar economic regions for consideration. By implementing these strategic recommendations, Bangladesh can overcome the barriers to adoption and unlock the full potential of BIM for a more efficient, innovative, and sustainable construction industry.

Disclosure

statement

There is no potential conflict of interest.

Funding

This research received no external funding.

Clinical trial number

Not applicable

Ethical Approval

and accordance

The research protocol was reviewed by the Institutional Research Ethics Board (IREB) of Dhaka University of Engineering & Technology (DUET), Gazipur, Bangladesh (Review ID: DUET-IREB-CE-6-230824).

In accordance with the committee's guidelines, the requirement for full ethical approval was waived under the Exempt Review category because the research was non-interventional and ensured participant anonymity.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

An information sheet detailing the study's objectives, voluntary nature, and confidentiality measures was provided to all respondents, who confirmed their consent via a mandatory tick-box before proceeding with the online survey.

All participants were confirmed to be over the age of 18, making parental or legal guardian consent not applicable.

Data Availability

The survey data are not publicly available, as participants were assured that their responses would be used for statistical analysis within this study only. The case study data are subject to legal restrictions; however, their use for academic purposes was approved by the relevant authorities.

Author Contribution

Shanto AA: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization.Manjur KA: Conceptualization, Methodology, Validation, Writing - Review & Editing, Supervision.Ananta AF: Formal analysis, Investigation, Data Curation.Das S: Validation, Formal analysis.Rahman M: Investigation.All authors have read and agreed to the published version of the manuscript.

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