Identifying the agile project management implementation barriers for sustainable residential buildings

Abstract

Incorporating sustainability concepts at all decision-making stages in the construction of residential buildings is essential to maximize benefits without compromising functionality. This study aimed to identify and analyze the obstacles hindering the application of Agile Project Management (APM) in residential construction projects. The research specifically addressed the following questions: What are the key barriers to APM adoption in Nigerian residential construction projects? How do these barriers impact the effectiveness of APM in promoting sustainability? A questionnaire survey was distributed to 120 construction professionals in Nigeria, capturing insights on APM challenges. Exploratory factor analysis was employed to categorize these obstacles into four main areas: social, economic, human factors, and culture and expectations. Subsequently, partial least squares-structural equation modeling was used to construct a model assessing the significance of these barriers. The findings revealed that economic factors, with a statistical significance coefficient of 0.516, are the most significant barriers to APM adoption. Social factors, cultural expectations, and human factors also play notable roles, though to a lesser extent. The outcomes provide valuable guidance for decision-makers in Nigeria’s construction sector, emphasizing the need for strategies that address economic constraints and foster greater adoption of APM practices to achieve cost reduction and enhanced sustainability.

Keywords

Building projects construction revolution agile project management project success sustainability

Introduction

The construction industry is a critical sector in global economic development, significantly influencing infrastructure projects in both developed and developing nations. This sector accounts for over 40% of global electricity consumption and approximately 30% of worldwide greenhouse gas emissions, making it one of the most resource-intensive industries.¹ In developed regions such as Europe and the United States, the construction sector’s energy consumption can reach up to 40% of total usage.² In contrast, in developing countries, particularly in Africa, the construction industry plays a vital role in improving living standards and is viewed as a key driver for economic diversification and growth through infrastructure and manufacturing investments.³

However, despite its significance, the construction industry in many developing countries struggles to meet international quality standards, highlighting a need for enhanced project management practices.⁴ The industry’s reliance on traditional project management methods, such as the waterfall approach, often exacerbates inefficiencies due to its rigid, sequential structure that lacks flexibility and adaptability.^5,6 These outdated methods hinder the effective management of risks, delay project timelines, and limit the integration of stakeholder feedback, making it difficult to meet modern demands, particularly in sustainable construction.^7,8

Agile Project Management (APM), which is known for its flexibility, iterative processes, and focus on continuous improvement, offers a potential solution to these challenges. Originally developed for software development, APM has increasingly gained attention in the construction sector due to its ability to enhance adaptability, client satisfaction, and project transparency.⁹ Key principles of APM, such as self-managing teams, iterative feedback loops, and emphasis on collaboration, can improve risk management and help align project timelines more closely with project goals.^10,11 Furthermore, by adopting APM, construction projects can more easily incorporate sustainable practices, which are becoming increasingly important in the face of environmental and resource challenges.¹²

However, the adoption of APM in the construction industry, particularly in developing economies like Nigeria, faces several barriers. Human factors, including a lack of expertise in APM, insufficient training, and issues with team dynamics, are significant obstacles.^13,14 Additionally, social factors, such as ineffective stakeholder engagement and poor communication, complicate the collaborative processes that APM relies on.¹⁵ Economic barriers like resource constraints, fluctuating costs, and financial instability further challenge the implementation of agile methodologies.¹⁶ Finally, cultural resistance to change, especially in industries that have long relied on hierarchical structures, remains a substantial hurdle to the widespread adoption of APM in construction.^12,17

While sustainability considerations are increasingly relevant to the construction sector, and APM can support these goals by enhancing project adaptability and efficiency, this study focuses on identifying and addressing the specific barriers to APM implementation. Through a deeper understanding of these barriers, particularly in the context of Nigeria’s residential construction sector, this research aims to provide practical recommendations for overcoming these challenges and facilitating more effective project management practices.^7,11

In this context, this research aims to identify and overcome barriers to APM in residential construction, thereby improving project success and sustainability. By focusing on this crucial yet underexplored area, the study seeks to deepen the understanding of how agile practices can be specifically tailored and applied effectively in residential construction.

Therefore, this study aims to achieve the following objectives:

1. Identify the significant barriers to APM implementation in the Nigerian construction industry, specifically within residential construction projects.

2. Analyze the data collected from construction professionals using Exploratory Factor Analysis (EFA) and Partial Least Squares-Structural Equation Modeling (PLS-SEM).

3. Prioritize the key barriers to APM adoption to suggest effective strategies for overcoming these barriers.

4. Provide practical recommendations for stakeholders in the Nigerian construction industry to enhance project management practices and achieve sustainability objectives.

The remainder of this paper is structured as follows: The barriers hindering the implementation of APM in construction are discussed in the next section. The research method, including data collection and case study, common method variance, construct validity analysis, measurement model, and structural model, is outlined in the following section. Data analysis is presented, addressing common method bias, exploratory factor analysis, measurement model validity (convergent and discriminant), and structural model analysis. A detailed discussion of the results follows, focusing on economic, social, cultural, and human factors. Finally, the implications of the findings for theoretical and practical perspectives are explored, and the study concludes with suggestions for future research.

Barriers hindering the agile project management implementation in construction

APM has brought about transformative changes in various sectors, especially software development, due to its focus on flexibility, efficiency, and stakeholder satisfaction. However, implementing APM in the construction industry introduces a unique set of challenges due to the sector’s inherent complexities and demands. This thematic literature review categorizes these challenges to provide a structured overview, making it clearer how each affects the adoption of APM in construction.

Project management and scheduling challenges

The agile iterative approach, beneficial in software development, complicates task scheduling within construction projects. This complexity arises as agile discourages detailed plans in the early stages, making it difficult to forecast overall project duration and impacting resource procurement. This uncertainty exposes projects to risks such as price fluctuations and scheduling issues, underscoring the importance of robust project schedules for successful execution and scope control.^15,18,19

Economic and financial constraints

Agile methodologies introduce a level of unpredictability in project costs and schedules due to their flexible nature, which can accommodate changes but also lead to economic challenges like price fluctuation. These issues require a careful balance between the inherent flexibility of agile methods and the defined project scopes, budgets, and timelines critical to the construction industry, highlighting the economic tensions faced in APM.^18,20

Human factors and team dynamics

Effective collaboration and communication are central to the success of agile projects, which emphasize the human element over strict processes. However, implementing agile principles in construction, especially in large teams, introduces additional coordination challenges due to limited interaction among participants and complex interdependencies. These factors complicate the fostering of effective collaboration in traditionally hierarchical environments.^15,21

Cultural and organizational barriers

Adopting agile methodologies requires overcoming significant cultural and organizational barriers, including resistance to change rooted in long-held values and traditional power dynamics. This resistance is driven by fear of the unknown and a sense of loss of control, necessitating a fundamental shift in team engagement, decision-making, and prioritization. Such changes challenge established organizational structures and routines, requiring extensive training and reassurance to facilitate the adoption of agile principles.^22–24

Client and external stakeholder engagement

Managing client expectations under agile methodologies poses considerable challenges as clients may have predefined expectations regarding project scope, budget, and timing that do not align with the iterative and adaptive nature of agile. Regular involvement and open communication are necessary to educate clients on the benefits of flexibility and adaptability, making stakeholder management a critical yet challenging aspect under agile methodologies.^22,25,26

Regulatory and technological challenges

The adoption of agile in construction must navigate regulatory and contractual constraints that demand a compromise between agile flexibility and the industry’s stringent requirements. This often involves adjusting agile practices to incorporate necessary documentation and compliance measures, alongside renegotiating contracts to accommodate an agile approach. Additionally, the lack of advanced tools and technology tailored for agile project management in construction hampers effective implementation.^27,28

Knowledge management and documentation

Agile practices encourage less formalized communication channels and minimal documentation, which can disrupt traditional power dynamics and make it difficult for organizations to maintain control over project information. The challenge of knowledge management is compounded by the need to balance adequate documentation, which is essential for maintaining clarity and continuity, with the agility required in agile projects. This balance is critical to ensuring that agile projects remain responsive to change while providing a reliable framework for project execution.^29,30

Addressing these challenges requires a nuanced understanding of both agile principles and the specific demands of construction projects, underscoring the need for adaptability, effective communication, and a commitment to continuous learning and improvement. Table 1 shows the most significant barriers to APM implementation in the construction industry, categorized by “SR” (Serial Number) indicating the item’s sequence, “B” (Barrier) for naming each specific obstacle, and “Studies” listing the research that supports the identification of these barriers.

Table 1.

Summary of barriers affecting the adoption of APM in construction.

SR	Barrier	Studies
B1	Scheduling of tasks	18,19
B2	Monitoring and control of the scope of the project	15
B3	Price fluctuation	18,31
B4	Challenge of knowledge management	18,31
B5	Issue of cost and time management	18,20
B6	Issue of collaboration and communication	15,21
B7	Challenge in large teams present on a project	19,32
B8	Challenge in implementing APM in large-scale projects	33,34
B9	Adoption at the construction phase of a project	35
B10	Problem of testing a building before completion	36,37
B11	Harder implementation of changes at the construction phase	38
B12	Changing deep-rooted organizational cultures	39,40
B13	Resistance to change from project teams	23,24
B14	Clients expectations	22,25
B15	Lack of awareness among construction professionals	22,25
B16	Regulatory and contractual constraints	26
B17	Lack of experienced agile practitioners in construction	41,42
B18	Inadequate tools and technology for implementing APM in construction	22,25
B19	Documentation and reporting	27,28
B20	Stakeholders management	29

Research method

Figure 1 provides a visual representation of the research design. The literature review, summarized in Table 1, identified 16 significant obstacles hindering APM effectiveness. Following this, a questionnaire survey was distributed among residential construction professionals with relevant industry experience, aiming to gather insights into barriers to APM. EFA was employed to assess the comprehensiveness and clarity of the identified APM barriers. EFA was chosen due to its ability to identify underlying relationships between observed variables and reduce data complexity, which helps in grouping the barriers into coherent factors. This method was applied to ensure that the barriers were well-defined and to refine the factors used in subsequent analyses. For determining the significance of the APM barriers, PLS-SEM was used due to its suitability for handling complex models with multiple constructs. This method was chosen due to its ability to handle both formative and reflective measurement models. It is particularly valuable in research fields where the theoretical knowledge is still developing, as it allows for greater flexibility in modeling complex relationships. In recent years, this method has attracted considerable attention across various fields, notably in business research and social sciences.⁴³ In this study, the latest version of SMART-PLS 3.2.7 was utilized to perform the analysis, evaluating both the measurement model and the structural model.

Figure 1.

Research design.

Data collection and case study

This study investigates the barriers affecting the adoption of APM within Nigeria’s residential construction industry. To ensure the representativeness and relevance of our findings, we conducted a comprehensive demographic analysis of the survey participants. As detailed in Table 2, the respondents comprise a diverse mix of professionals, including contractors (28%), consultants (32%), clients (23%), as well as architects, engineers, and quantity surveyors (17%). This varied demographic profile is crucial for capturing a broad spectrum of insights and experiences pertinent to APM implementation. Additionally, participants with diverse levels of experience were selected: 37% had less than 5 years of experience, 32% had between 5 and 10 years, and 31% had more than 10 years of experience. Educational qualifications were varied, with 50% holding a bachelor’s degree, 36% holding a master’s degree or higher, and 9% with a high school diploma. The age range of respondents was well distributed, with 14% under 25, 46% between 25–34, and 23% between 35–44, with smaller percentages in older age brackets.

Table 2.

Demographic profile of survey respondents.

Demographic feature	Categories	Number of respondents	Percentage (%)
Professional role	Contractors	30	28
	Consultants	35	32
	Clients	25	23
	Others (Architects, Engineers, Quantity surveyors)	19	17
Experience	Less than 5 years	40	37
	5 to 10 years	35	32
	More than 10 years	34	31
Educational background	High school diploma	10	9
	Bachelor’s degree	55	50
	Master’s degree or higher	39	36
	Other certifications	5	5
Age group	Under 25	15	14
	25–34	50	46
	35–44	25	23
	45–54	10	9
	55 and above	9	8

To collect this data, we employed two non-probability sampling methods: purposive (judgment) sampling and snowball sampling. Purposive sampling was particularly efficient and cost-effective, enabling the selection of participants directly involved in or knowledgeable about APM in the construction sector. Conversely, snowball sampling expanded our coverage by leveraging the networks of initial respondents, who recommended additional professionals. This method effectively broadened the diversity of viewpoints by creating a referral network that enriched the study’s data set.

The survey was structured into three sections: (1) respondents’ demographic information; (2) inquiries about the APM barriers listed in Table 1; and (3) open-ended questions to identify any barriers deemed crucial by the respondents. The APM barriers were evaluated using a 5-point Likert scale, with 5 indicating “extremely high”, 4 signifying “high”, 3 meaning “moderate”, 2 signifying “low”, and 1 indicating “none or very low.” The questionnaire used in this study was developed based on an extensive review of the literature on APM and its barriers in the construction industry. Key themes and constructs were identified from previous studies and used to design the survey items. To ensure content validity, the initial draft of the questionnaire was reviewed by a panel of five experts in the field, including three academic researchers specializing in construction management and sustainability, and two senior professionals with over 10 years of experience in construction project management. Their feedback was incorporated to refine the questions and ensure they were clear, relevant, and comprehensive.

To further ensure the reliability and validity of the instrument, a pilot test was conducted with a small sample of 15 construction professionals from different roles (contractors, consultants, and engineers) in the industry. The results of the pilot test were used to assess the clarity, relevance, and appropriateness of the questions. Based on the feedback from the pilot test, minor adjustments were made to improve the clarity and structure of several items. The final version of the questionnaire consisted of 25 closed-ended questions evaluated using a 5-point Likert scale, along with two open-ended questions to capture any additional barriers the respondents deemed relevant.

To determine the appropriate sample size, the methodological analysis recommended by Badewi⁴⁴ suggested that a sample size exceeding 100 was suitable for survey studies. A total of 109 responses were obtained from 120 individuals, resulting in a response rate of 90%. This response rate was considered satisfactory based on the findings of previous studies.^45,46

While the demographic diversity of the sample provides valuable insights, the sample size of 109 respondents may not fully represent the broad construction sector in Nigeria. Future research could enhance the generalizability of the findings by incorporating a larger and more varied sample, possibly including respondents from additional sectors or regions within the country.

Common method variance

Common Method Bias (CMB) was assessed by calculating Common Method Variance (CMV). CMB serves to highlight inaccuracies in research findings that stem from the measurement method used rather than the constructs the measures are intended to represent. This emphasis on the measurement method differentiates CMB from the constructs. Additionally, CMV can be viewed as an overlap in variance attributable to both the constructs under study and the types of measurement instruments utilized, offering an alternative perspective on CMV. The issue of CMV becomes particularly acute in cases where data collection relies on a single source, such as responses gathered from a questionnaire.^47,48

In some instances, the use of self-report data may either amplify or diminish the scope of the relationships under study, thereby introducing challenges.^48,49 This issue becomes particularly pertinent when considering that all data is derived from subjective self-reports and originates from a singular source. Hence, addressing these challenges in identifying common method modifications is crucial. The systematic one-factor test ascertains whether factor analysis reveals a solitary factor responsible for the majority of the variance.⁴⁸

Construct validity analysis

Factor analysis is typically conducted using either Confirmatory Factor Analysis (CFA) or EFA. In this study, CFA was utilized to assess the structure underlying the various variables proposed in the hypotheses and theories. Conversely, EFA is applied to explore the relationships among multiple variables and simplify numerous variables into fundamental structures.⁵⁰ EFA is designed primarily for data that are either interval or ordinal. A scatterplot was used to demonstrate the degree to which the variables were partially or fully related. This method aims to simplify analysis by reducing the number of components, thereby representing a set of variables with fewer dimensions. The equation employed in the EFA process is shown in equation (1).

X_{i} = a_{i 1} F_{1} + a_{i 2} F_{2} + \dots + a_{i m} F_{m} + e_{i}

(1)

In this equation, $X_{i}$ represents the ith standardized variable to be measured, $a_{i}$ denotes the factor loading (or score) for the ith variable, $F$ is the factor under investigation, and $e_{i}$ refers to the portion of the variable that remains unexplained by the factors.

EFA is a pivotal multivariate analytic technique that facilitates the investigation of the underlying structures within APM barrier elements. Its application is critical for evaluating the constructs’ measurement items in terms of unidimensionality, reliability, and validity (i.e., measurement models). Notably, Principal Component Analysis (PCA) was preferred over alternatives such as principal axis factoring, image factoring, maximum likelihood, and alpha factoring, owing to its enhanced precision and reduced complexity.⁵¹

When EFA suggests possible solutions without an existing theory or model to support them, PCA emerges as an advantageous approach.⁵⁰ Thompson⁵² noted that PCA’s adoption as a default in numerous statistical software makes it a common choice for conducting EFA. The research opted for a varimax rotation instead of simpler rotations such as oblimin or Promax, aiming for an equitable distribution of workload among variables. Varimax rotation is praised for its ability to improve factor clarity, making it an excellent choice for basic factor analysis.⁵³ A sample size of 119 and the inclusion of 17 variables were deemed adequate for the factor analysis process.⁵⁴

Measurement model

The measurement model reveals both the latent structure of the items and their existing relationships.⁵⁵ The following sections comprehensively examine the convergent and discriminant validity of the measurement model.

• Convergent validity: Convergent validity is defined as the degree to which different measures (barriers) of the same construct are in agreement. Hair et al.⁵⁶ represent a component of construct validity. To assess the convergent validity of the constructs calculated via PLS, three methods were utilized: Cronbach’s alpha (α), composite reliability scores ( $ρ_{c}$ ), and the Average Variance Extracted (AVE).⁵⁷ Nunnally⁵⁸ suggested that $ρ_{c}$ value of 0.7 indicates a moderate level of reliability for the composite. For all types of research, values exceeding 0.70 were considered acceptable, while values above 0.60 were acceptable for exploratory research. AVE was the final method employed. This technique is frequently used to assess the convergent validity of model constructs, with values greater than 0.50 signifying adequate convergent validity.⁵⁹

• Discriminant validity: Discriminant validity confirms that the phenomenon being studied is distinct in empirical terms and not represented by any other measures.⁶⁰ In the process of validating discriminant validity, Campbell and Fiske⁶¹ contended that the similarity between different measures should be minimal.

Structural model

This study applied SEM to identify the most significant barriers to APM. The critical step involves calculating the path coefficients among the observed variables. It was hypothesized that £ (structures of APM barriers) directly influence µ (implementation barriers of APM). A linear equation, represented in equation (2), was utilized to illustrate the internal dynamics between the £, µ, and €1 structures within the structural model.⁶⁰

µ = β £ + € 1

(2)

In this context, (β) represents the path coefficient connecting the constructs of APM barriers, with the residual variance in this structural layer assumed to be captured by (€1). The standardized regression weight (β) was analogous to the β coefficient in the multiple regression analysis.

The pivotal aspect of this analysis was the evaluation of the significance of the path coefficient (β). Similar to the processes used in CFA, this investigation calculated the path coefficients’ standard errors using a bootstrapping technique available in SMART PLS 3.2.7 software, as per the recommendations of,⁴³ the generation of t-statistics for hypothesis evaluation utilized 5000 bootstrap samples. Within the PLS model, equation (2) was employed to develop four structural equations related to the constructs of APM barriers, each detailing the interrelationships between these constructs.

Data analysis

Common method bias

CMB, a measurement error variance, threatens the validity of research findings by introducing systematic errors in both measured and predicted variables.⁵³ The extent of this bias can be determined using Harman’s single-factor test, which assesses various structural elements.⁶² This research applied a single-factor test to assess the standard deviation.⁶³ If the aggregate variance explained by the factors is less than 50%, this suggests that CMB may not significantly influence the results. Given that the initial group of components explains only 32.0% of the total variance, it is evident that the results are unlikely to be distorted by the CMV, remaining below the 50% threshold.⁶²

Exploratory factor analysis

EFA was employed to investigate the factor structure of 16 items related to barriers to APM. This analysis utilized several widely recognized measures of factorability. Typically, the Kaiser-Meyer-Olkin (KMO) measure is applied to assess the adequacy of sampling for factor analysis by ensuring that the partial correlations among variables are minimal.⁶⁴ For factor analyses to be deemed acceptable, the KMO index must exceed 0.6.⁶⁵ Furthermore, Bartlett’s test of sphericity confirms the non-suitability of the identity matrix as a correlation matrix at a significance level of p < 0.05.^66,67

As shown in Table 3, the KMO measure of sampling adequacy indicated that the data were suitable for factor analysis, and Bartlett’s test of sphericity demonstrated a highly significant correlation adequacy among the variables. A KMO value of 0.904 implies that 90.4% of the data collected were appropriate for factor analysis.

Table 3.

Kaiser-Meyer-Olkin and Bartlett’s test coefficients of the barriers.

KMO and Bartlett’s test
Kaiser-Meyer-Olkin measure of sampling adequacy		0.904
Bartlett’s test of sphericity	Approx. Chi-Square	1482.685
	df	190
	Sig.	0

Additionally, Table 3 reveals a p-value < 0.05, indicating the suitability of the data for factor analysis with 190 degrees of freedom and an approximate chi-square value of 1482.685. The highly significant p-value (0.000) from Bartlett’s test suggests that the correlation matrix does not act as an identity matrix, indicating a significant correlation among all the listed items at the 5% level. Thus, EFA was deemed suitable for this dataset.

The results of PCA, as shown in Table 4, revealed four components with eigenvalues exceeding one. These components explained 26.30, 25.53, 9.30, and 8.38% of the variance, respectively. Additionally, a review of the scree plot in Figure 2 highlights a distinct demarcation following the second component, suggesting that the analysis should yield four-factor groups. This demarcation is evidenced by the flattening of the curve, which indicates the optimal number of factors to be extracted. Table 5 displays the rotated component matrix for the issues related to APM, categorizing them into four distinct factors: F1, F2, F3, and F4.

Table 4.

Total variance explained for the current problems.

Component	Initial eigenvalues	% of variance	Cumulative %	Rotation sums of squared loadings
Component	Initial eigenvalues	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	9.465			5.26	26.301	26.301
2	1.742			5.107	25.534	51.836
3	1.551			1.86	9.301	61.137
4	1.146	5.728	69.519	1.676	8.382	69.519
5	0.892	4.459	73.978
6	0.736	3.68	77.657
7	0.597	2.986	80.643
8	0.573	2.866	83.51
9	0.49	2.449	85.959
10	0.449	2.245	88.204
11	0.385	1.924	90.128
12	0.351	1.756	91.884
13	0.304	1.521	93.405
14	0.258	1.29	94.695
15	0.227	1.137	95.831
16	0.222	1.111	96.942
17	0.191	0.953	97.896
18	0.163	0.816	98.711
19	0.148	0.741	99.452
20	0.11	0.548	100
Extraction method: Principal component analysis

Figure 2.

Scree plot loading of the current problems of the APM adoption.

Table 5.

Rotated component matrix of the current problems.

Variables	F1	F2	F3	F4
Scheduling of tasks		0.62
Monitoring and control of the scope of the project		0.463		0.412
Price fluctuation		0.706
Challenge of knowledge management	0.424	0.733
Issue of cost and time management	0.479	0.717
Issue of collaboration and communication			0.77
Challenge in large teams present on a project		0.728
Challenge in implementing APM in large-scale projects	0.405	0.711
Adoption at the construction phase of a project		0.681
Problem of testing a building before completion	0.411	0.66
Harder implementation of changes at the construction phase		0.583
Changing deep-rooted organizational cultures				0.843
Resistance to change from project teams			0.76
Clients expectations				0.722
Lack of awareness among construction professionals	0.768
Regulatory and contractual constraints	0.849
Lack of experienced agile practitioners in construction	0.775	0.425
Inadequate tools and technology for implementing APM in construction	0.867
Documentation and reporting	0.786
Stakeholders management	0.796

The process of assigning names to these factors is inherently subjective and lacks a definitive methodology for factor naming. Consequently, the careful selection of names for these factors was considered suitable for this research. The factors were interpreted and named as indicated in Table 6: social factors, economic factors, human factors, culture, and expectations.

Table 6.

Related components of the current problems with the adoption of APM in construction projects.

Factor component	Code	Variables	Factor loading
Social factor	B15	Lack of awareness among construction professionals	0.768
	B16	Regulatory and contractual constraints	0.849
	B17	Lack of experienced agile practitioners in construction	0.775
	B18	Inadequate tools and technology for implementing APM in construction	0.867
	B19	Documentation and reporting	0.786
	B20	Stakeholders management	0.796
Economic factor	B1	Scheduling of tasks	0.62
	B2	Monitoring and control of the scope of the project	0.463
	B3	Price fluctuation	0.706
	B4	Challenge of knowledge management	0.733
	B5	Issue of cost and time management	0.717
	B7	Challenge in large teams present on a project	0.728
	B8	Challenge in implementing APM in large-scale projects	0.711
	B9	Adoption at the construction phase of a project	0.681
	B10	Problem of testing a building before completion	0.66
	B11	Harder implementation of changes at the construction phase	0.583
Human factor	B6	Issue of collaboration and communication	0.77
Human factor	B13	Resistance to change from project teams	0.76
Culture and expectations	B12	Changing deep-rooted organizational cultures	0.843
Culture and expectations	B14	Clients expectations	0.722

Reliability metrics were established for the factors identified through EFA. The assignment of variables to each factor was based on the highest loadings within the structural matrix. According to Ref. 58, a Cronbach’s alpha value of greater than 0.6 is required for newly developed scales. Conversely, an average value of 0.7 suggests that values exceeding 0.75 are deemed highly reliable. Given that Cronbach’s alpha values exceeded 0.6, the results can be considered dependable. Furthermore, the average correlations among items exceeded 0.3, demonstrating consistent internal coherence among variables.

Measurement model

In SEM-PLS analysis of reflective measurement models, it is essential to assess internal consistency, convergent validity, and discriminant validity. Once the validity and reliability of the measurement model are confirmed, an evaluation of the structural model follows.⁶⁰

Convergent validity

Table 7 indicates the validity of all the model constructs, as evidenced by their α and

ρ_{c}

values exceeding 0.70.⁶⁸ Moreover, Table 7 confirms that all constructs meet the AVE criteria, with AVEs above 0.5, deemed satisfactory.⁵⁷ These results demonstrate that the measurement model exhibits both internal convergence and consistency. This signifies that, within the model of this study, each construct (or group) is effectively measured by its designated set of items. A high outer loading of an item indicates strong association and relevance to its corresponding elements. Generally, items with outer loadings below 0.4 are viewed as having inadequate representation and are recommended to be excluded from the scale.⁶⁸ Figure 3 displays the outer loadings for the measurement models of all items, indicating that all external loadings were within the acceptable ranges.

Table 7.

Convergent validity.

Constructs	Cronbach’s alpha	Composite reliability	Average variance extracted
Culture and expectations	0.814	0.915	0.843
Economic	0.959	0.965	0.733
Human	0.8	0.909	0.833
Social	0.932	0.946	0.746

Figure 3.

PLS-SEM model.

Discriminant validity

As shown in Table 8, the square root values of the AVEs exceeded their correlations with all other constructs, indicating an absence of association between any two constructs. Additionally, these values confirm that each predictor achieved the highest loading on its respective construct, as detailed in Table 8. This ensured a high level of unidimensionality for each construct.

Table 8.

Discriminant validity.

Constructs	Culture and expectations	Economic	Human	Social
Culture and expectations	0.918
Economic	0.882	0.856
Human	0.82	0.893	0.913
Social	0.847	0.905	0.829	0.864

Structural model

Upon identifying the APM barriers as a formative construct, the analysis assesses collinearity among the constructs’ formative indicators by examining the variable inflation factor values. With all variable inflation factor values significantly below 3.5, each subdomain contributes independently to the overarching construct. Additionally, bootstrapping was employed to evaluate the significance of path coefficients, with results showing statistical significance at the 0.01 level for all paths.⁶⁹ Notably, as illustrated in Figure 4 and Table 9, all constructs related to partnership are significant.

Figure 4.

Path model.

Table 9.

Path analysis results.

Paths	B	p values
Culture and expectations → Agile project management implementation barriers	0.11	0
Economic → Agile project management implementation barriers	0.516	0
Human → Agile project management implementation barriers	0.109	0
Social → Agile project management implementation barriers	0.305	0

Discussion

Although APM in the construction sector is extensively utilized in many developed countries, its adoption in developing nations, including Nigeria, is just starting to emerge. The construction quality in these emerging economies faces various challenges and contradictions. The introduction of new methodologies is expected to markedly influence the construction sector; however, assessing the impact and potential advantages of APM remains challenging.⁷⁰ Therefore, the application of APM principles is crucial to address these challenges. The likelihood of senior management endorsing the integration of APM into project frameworks increases when practitioners are well-informed about APM and its critical construction processes. The proposed model identifies four key dimensions of APM that have a significantly positive influence on its adoption. This adoption is intended to enhance the sustainability of residential construction projects. Consequently, adopting APM can lead to cost and time savings while elevating the quality without compromising the essential functions of the project. The subsequent sections detail the use of the PLS-SEM model to prioritize barriers to APM adoption.

Economic

The significance of building project characteristics cannot be overstated. According to the PLS-SEM model, the “economic” factor, with an external coefficient of 0.516, is identified as having the most substantial influence on the barriers to APM implementation. This factor encompasses 10 elements: task scheduling, scope monitoring and control, fluctuations in pricing, knowledge management issues, cost and time management issues, challenges with large project teams, difficulties in applying APM to large-scale projects, implementation during the construction stage, pre-completion testing challenges, and complications making changes during the construction phase. This insight aligns with,¹⁸ who asserted that scheduling project tasks presents a notable hurdle for APM. Agile methodology advises against setting fixed specifications and plans at the outset of a project, which complicates quality assurance and control owing to the absence of predefined benchmarks for monitoring and validation. Similarly, Owen et al.³⁵ found that although agile methodologies are applicable in the pre-design and design stages of construction projects, their adoption faces significant obstacles in the construction phase. Furthermore, Xu³² highlighted severe coordination issues in implementing agile principles within large-scale projects attributed to limited participant interaction, communication challenges, technical complexities, and intricate task interdependencies. The use of agile methodologies in sectors beyond IT, engineering, and product development presents significant challenges.

Social

“Social” factors were identified as the second principal component, encompassing obstacles such as construction professionals’ lack of awareness, regulatory and contractual limitations, scarcity of construction-experienced agile practitioners, insufficient tools and technology for APM application in construction, challenges in documentation and reporting, and stakeholder management. This observation corroborates the view that construction personnel, who are typically more familiar with conventional project management techniques, might not be well-acquainted with agile methodologies prevalent in software development.⁴² This unfamiliarity can hinder the effective adoption of agile practices in construction projects. A significant impediment is the overall dearth of agile methodology expertise and exposure in the construction sector. Traditional project management methods are the foundation of most construction professionals’ training, leaving them with little or no exposure to agile principles during their education or career development.

Cultural and expectations

The third key component pertains to “cultural and expectations”, addressing obstacles such as the transformation of entrenched organizational cultures and managing client expectations, with two items specifically mentioned. This insight is in line with³⁹ analysis, which suggests that the adoption of agile methodologies requires a significant shift in team interaction, decision-making, and prioritization processes. Such changes can challenge existing organizational frameworks, communication paths, and traditional norms, leading to resistance and hindrances. Furthermore, Ciric et al.²⁵ highlighted that aligning APM with client expectations poses a substantial challenge. Agile methods, owing to their iterative and flexible nature, may not align with clients’ conventional expectations regarding project scope, budget, and timelines. Given that clients may hold specific preconceived standards and may not be well-versed in agile practices, ensuring that their expectations are met and managed is crucial.

Human

The fourth category in the hierarchy of obstacles to APM implementation is labeled “knowledge”, with an external coefficient of 0.138. This category encompasses barriers including deficiencies in awareness and market understanding. Human factors in APM can be broadly understood within the framework of socio-technical systems, which integrate social and technical elements. Previous research has extensively explored these systems, highlighting various aspects of human factors in different contexts. For instance, Ogbeyemi et al.⁷¹ investigated human factors among workers in small manufacturing enterprises, while Aloui and Shams Eldin⁷² examined socio-emotional competencies and their impact on employability. The scant awareness of APM constitutes a significant impediment to its integration into the construction sector, particularly in emerging economies. This issue may stem from negative perceptions of new technologies.⁷³ It is challenging for new technological solutions to gain recognition in the construction industry.⁷⁴

This study identified several key barriers to APM adoption in Nigeria’s residential construction sector, focusing on economic, cultural, and human factors that hinder both project success and sustainability efforts. The findings suggest that economic factors such as price fluctuations and cost management challenges are the most significant barriers to APM implementation. Cultural factors, including resistance to change and entrenched organizational norms, as well as human factors like inadequate collaboration and lack of APM awareness, also play critical roles in limiting the adoption of agile practices.

These findings align with and extend prior research on APM implementation in various sectors, while also highlighting unique barriers specific to the construction industry in developing economies. Previous studies, such as those by Owen et al.³⁵ and Xu,³² identified the challenge of integrating APM into large-scale projects, particularly in traditional industries like construction, where rigid structures dominate. Our results are consistent with these findings, particularly the economic and human factors that hinder the flexibility required for agile practices. For instance, the economic barriers identified in this study, such as price fluctuations and cost management challenges, reflect the unpredictability that Owen et al.³⁵ also observed as an obstacle to agile adoption during the construction phase.

Furthermore, Ciric et al.²⁵ emphasized cultural resistance as a significant barrier to APM implementation in construction, which is supported by our findings. In Nigeria, entrenched organizational cultures and general resistance to change were identified as critical challenges, reinforcing the literature that highlights the difficulty of transforming hierarchical structures in traditional industries to accommodate agile principles. This cultural barrier is particularly pronounced in developing countries, where familiarity with APM is still growing, as Taylor²⁶ observed in her study on the transition to agile methodologies.

However, our study also extends previous literature by focusing specifically on sustainability in construction. While Masood and Farooq¹⁵ explored the benefits of agile practices in promoting flexibility, our findings emphasize the additional challenges posed by the integration of sustainability objectives within APM frameworks. For example, economic and regulatory barriers not only affect the agility of project management but also directly impact the ability to meet sustainability goals, particularly in resource-constrained environments like Nigeria. This gap in previous research highlights the need for a more nuanced understanding of how APM can support sustainable construction, which is a critical concern for developing countries.

Our findings also diverge from some prior studies in terms of the social and human factors influencing APM adoption. While Boehm and Turner³⁶ noted that agile methodologies are highly adaptable to sectors involving frequent stakeholder interaction, the limited awareness and expertise in APM among Nigerian construction professionals pose a unique challenge. This suggests that APM frameworks developed in more mature markets might require significant adaptation when applied to the construction sectors of developing economies.

By situating our results within the broader context of APM literature, this study not only reinforces existing theories but also highlights new areas for future research. Specifically, the intersection of APM, sustainability, and construction in developing countries presents opportunities to explore how agile practices can be tailored to meet both economic and environmental goals.

Implications

Theoretical implications

This study makes significant contributions to the existing body of knowledge on APM and sustainable construction, particularly in the context of developing economies. While APM has been extensively studied in software development and other sectors, its application in the construction industry, especially in emerging markets like Nigeria, is underexplored. This research fills a critical gap by identifying specific barriers to APM implementation in the construction sector and examining how these barriers affect sustainability outcomes. Furthermore, the study extends existing theories by integrating the concepts of sustainability and APM, providing a new lens through which future studies can assess the relationship between agile practices and sustainable construction. This study’s findings also offer a foundation for developing theoretical frameworks that address the intersection of APM, project success, and sustainability in resource-constrained environments.

Practical implications

From a practical standpoint, the findings of this research have direct applications for policymakers, construction managers, and industry stakeholders. The identified barriers to APM adoption—such as economic, social, cultural, and human factors—provide a roadmap for decision-makers to address these challenges in real-world projects. By understanding and mitigating these barriers, construction firms can enhance project outcomes, improve cost efficiency, and promote sustainability in their operations. The recommendations for overcoming economic constraints and fostering greater collaboration and communication within teams are particularly relevant for achieving sustainable practices in the construction sector. Moreover, this study’s insights offer actionable guidance on how to integrate APM methodologies in developing countries, enabling more effective project management and contributing to the broader sustainability agenda. These contributions are essential for the construction industry to achieve better project delivery, cost reduction, and long-term environmental responsibility.

Conclusion

APM has been widely recognized for its potential to optimize value, project outcomes, and sustainability, particularly in large-scale construction projects. However, its adoption in developing economies, including Nigeria, remains limited. This study examined the barriers hindering the APM implementation in Nigeria’s construction industry. Through an extensive literature review, we identified 16 critical barriers, which were subsequently refined using EFA and empirically validated through PLS-SEM. Data gathered from 120 construction professionals in Nigeria highlighted key obstacles to APM adoption and provided a foundation for overcoming these challenges.

The findings of this study contribute to both academic and industry practice by offering a framework of barriers to APM within the Nigerian construction sector. The results have significant implications for improving project delivery, reducing costs, and promoting sustainability through targeted APM strategies. The timing of this study is particularly critical, as Nigeria’s construction industry is currently experiencing rapid growth and facing increasing pressure to deliver sustainable housing solutions. Therefore, this research serves as a timely intervention to guide industry professionals toward adopting APM strategies that can address these demands. Despite these contributions, several limitations should be acknowledged:

1. Sample size and representation: The study’s sample size of 120 respondents, while providing valuable insights, may not fully represent the diverse range of stakeholders in the Nigerian construction industry. Future research could benefit from a larger and more varied sample to enhance the generalizability of the findings.

2. Data collection methods: The data was collected through a questionnaire survey, which may introduce biases such as self-reporting bias or response bias. These biases could affect the accuracy of the insights into APM barriers. Utilizing multiple data collection methods, such as interviews or case studies, could provide a more comprehensive understanding.

3. Regional focus: The research focused specifically on Nigeria, and the findings may not be directly applicable to other developing countries with different construction industry contexts. Comparative studies involving multiple countries could offer a broader perspective on APM adoption challenges.

Acknowledging these limitations provides a more balanced view of the study’s conclusions. Future research could address these limitations by employing larger and more diverse samples, incorporating multiple data collection methods, and exploring the broader implementation of APM in construction projects. Continued research and exploration in this field will contribute to advancing APM practices and enhancing sustainability in the construction industry.

Footnotes

Author contributions

All authors contributed to the study conception and design, data collection, analysis and interpretation of results, and manuscript. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2024/R/1445). Furthermore, the authors express their gratitude to the Middle East University in Amman, Jordan for providing financial support to cover the publication fees associated with this research article.

ORCID iDs

Ayodeji Emmanuel Oke

Nehal Elshaboury

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.*

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