Examining the impact of human factors on lean construction scheduling through network and feedback analysis

The construction sector is a cornerstone of global economic growth, contributing about 13% to global GDP and generating annual spending of nearly USD 10 trillion, projected to reach USD 14 trillion by 2025 (Mc Kinsey Global Institute, 2017). Despite its large scale, the industry continues to lag behind other sectors in productivity, with annual growth averaging only 1% compared to 3% in manufacturing (McKinsey Global Institute, 2024). As global demand for infrastructure, housing, and sustainable development intensifies, these inefficiencies create pressure to adopt innovative and waste-reducing practices (Weerapura et al., 2023). In New Zealand, the construction industry mirrors these global trends but faces additional local constraints. It contributes 6.3% to GDP and employs over 10% of the national workforce (MBIE, 2024). Yet productivity remains low due to factors such as housing markets and reliance on imported materials (Kirby et al., 2024). Further challenges include an ageing workforce and a persistent shortage of skilled labour (Kirby et al., 2025). These systemic pressures underscore the need for lean construction methods, which have shown significant benefits internationally but remain slow to be adopted and inconsistent in New Zealand, largely due to persistent human-related barriers.

Lean construction emerged in response to inefficiencies and cost overruns in project delivery (González et al., 2022). Its principles focus on maximising value for the client by eliminating non-value-adding activities, improving workflow reliability, and fostering collaboration among project stakeholders (Katō and Smalley, 2011). Internationally, lean methodologies such as the Last Planner System (LPS) and takt time planning have reported improvements of up to 25% in cost efficiency compared to traditional project management practices (Hasan et al., 2018). These approaches rely not only on technical scheduling tools but also on teams' collective commitment to honouring work promises, communicating effectively, and continuously improving performance (Warid and Hamani, 2023). In practice, however, lean construction often faces obstacles when applied outside of manufacturing because construction projects are temporary, involve multiple stakeholders with competing interests, and operate under uncertain site conditions. Tezel et al. (2018) note that lean is sometimes adopted superficially under client pressure, leading to inefficiencies and disillusionment. Similarly, Vecchio et al. (2023) emphasise that early collaboration and stakeholder engagement are crucial to project success, yet fragmented supply chains frequently undermine these practices.

A critical dimension of lean construction is the influence of human factors on scheduling practices. While many studies have focused on the technical application of lean tools, less attention has been given to how behavioural, organisational, and cultural variables shape implementation outcomes. Hughes and Thorpe (2014) argue that workforce resistance to change and insufficient training frequently derail lean initiatives, while Likita et al. (2024) highlight that poor teamwork and communication exacerbate waste and inefficiency. Kirby et al. (2025) further stress that weak leadership commitment and conservative attitudes towards innovation continue to hinder lean adoption in New Zealand. These findings reveal a significant gap in the literature: existing research often treats lean scheduling inefficiencies as technical issues, rather than recognising the systemic role of human barriers (Hasan et al., 2018; Kirby et al., 2024; Likita et al., 2024; Magnani et al., 2023). The consequence is that lean construction efforts frequently fail to realise their full potential, as individuals' behaviours, skills, and attitudes remain misaligned with the collaborative norms required for success.

This study directly addresses that gap by focusing on the human-related variables that affect lean construction scheduling in New Zealand. By analysing these human-centric challenges, the research aims to provide practical insights into how lean scheduling can be better adapted to local realities. The guiding research question is: What are the human factors affecting the efficiency of lean scheduling in the New Zealand construction industry, and what actions can be recommended to reduce their impact? Addressing this question not only contributes to the academic understanding of lean construction but also offers actionable strategies for industry stakeholders seeking to enhance productivity, reduce waste, and strengthen project performance.

The construction sector is vital to New Zealand's economy, accounting for 6.3% of GDP and employing more than 10% of the national workforce in 2023 (MBIE, 2024). Despite its importance, productivity remains low, with growth rates consistently lagging behind the OECD average (Curtis, 2018; New Zealand Productivity Commission, 2023). These challenges have intensified calls for innovative approaches to improve performance, with lean construction increasingly recognised as a viable solution (Kirby et al., 2024). Derived from the Toyota Production System, lean construction provides a structured framework to minimise waste, enhance workflow reliability, and strengthen collaboration (González et al., 2022; Likita et al., 2024). Techniques such as the Last Planner System and takt time planning have demonstrated substantial gains, including a 54% productivity improvement and early project completion in case studies (Johansen et al., 2021). Beyond efficiency, lean promotes safety, trust, and continuous improvement across project teams (Power et al., 2024; Sarhan et al., 2019). However, in New Zealand, adoption remains fragmented due to skill shortages, limited workforce capability, and cultural resistance to change, leading to partial implementation and reduced benefits (Tezel et al., 2018).

Several studies point to the persistent inefficiency of lean implementation in practice. Despite its theoretical promise, lean construction has not delivered consistent productivity gains across the sector, particularly in regions like New Zealand, where output has fluctuated, with construction's GDP contribution falling from 6.3% to 4.4% in 2023 (MBIE, 2024). Globally, construction productivity has remained stagnant since the mid-twentieth century, even as manufacturing and agriculture achieved transformative gains (Mc Kinsey Global Institute, 2017). Scholars emphasise that many of these shortcomings are linked not to the lean framework itself, but to barriers to its application, including weak leadership commitment, ineffective integration across supply chains, and insufficient workforce capability (Kirby et al., 2025; Likita et al., 2024). These observations suggest that while lean scheduling tools have proven technical value, their success depends on addressing the human and organisational conditions in which they are embedded. As such, research attention has increasingly shifted toward understanding the specific factors that hinder or enable lean scheduling performance.

Among the most critical influences are human-related factors. Studies identify multiple dimensions, including knowledge and skill, behaviour and attitude, communication and collaboration, and decision-making. Skill shortages, an ageing workforce, and insufficient training limit the industry's ability to adapt to new technologies and processes (Chen et al., 2023; Uhrin et al., 2017). Without adequate training, resistance to lean tools is common, as observed in Brazil, Lebanon, and other contexts (De Silva et al., 2025; Hamzeh and Albanna, 2019). Behavioural and cultural factors further complicate implementation, with low motivation, adversarial team cultures, and resistance to change frequently undermining collaboration (Evans and Farrell, 2021; Sarhan et al., 2019; Ugurlu et al., 2021). Poor communication and weak collaboration are repeatedly cited as global barriers that contribute to rework, safety issues, and significant productivity losses (Bayhan et al., 2019; Demirkesen and Bayhan, 2020; Yang et al., 2020). Finally, decision-making processes, which are often constrained by cognitive biases, limited information, or time pressures, can generate waste and heighten risks on site (Purushothaman and Seadon, 2024). While these variables are widely acknowledged in international studies, their interplay within the New Zealand context remains underexplored.

In summary, the literature demonstrates that lean construction has the potential to transform project delivery by reducing waste, enhancing workflow, and fostering collaboration. However, evidence also reveals persistent inefficiencies linked to human factors, such as ineffective training, workforce ageing, a conservative attitude, and poor communication. Although these challenges have been investigated in global studies (Chen et al., 2023; Enshassi et al., 2021; Magnani et al., 2023), there remains limited research examining their specific influence on lean scheduling within the New Zealand construction industry. This gap is significant given New Zealand's unique demographic profile, small and fragmented market, and reliance on imported labour and materials. Addressing this research gap is crucial to understanding how behavioural dynamics shape the implementation of lean scheduling locally, and to developing targeted strategies that can support contractors, consultants, and subcontractors in overcoming these barriers and improving project efficiency.

This study employed a qualitative research design that integrated a systematic literature review (SLR) with expert validation to examine people-centred factors affecting lean construction scheduling. The SLR, conducted in accordance with PRISMA guidelines (Page et al., 2021), ensured methodological transparency and rigour. Seventy-seven peer-reviewed studies published after 2020 were reviewed and thematically coded into eight categories: awareness, behaviour, collaboration, communication, decision making, organisation, skills, and training. The selected studies were sourced internationally and were not limited to the New Zealand context, ensuring that the identified human factors reflect globally reported lean construction challenges. Network analysis further enhanced the process by analysing frequency through an author–factor matrix, identifying key influences using eigenvector and degree of centrality measures, and visualising systemic relationships through causal loop diagrams. To validate and contextualise these findings, semi-structured interviews were held with five experienced professionals from New Zealand's construction industry. Their insights confirmed the relevance of key factors and provided context-specific understanding of how these human elements influence lean scheduling efficiency. Table 1 summarises the data sources, collection methods, and analytical approaches adopted in this study.

A systematic literature review (SLR) was undertaken as the primary strategy for collecting secondary data on human factors affecting lean construction scheduling. The review process began with the development of a keyword framework based on four pillars: lean construction, human involvement, influencing factors, and the construction context. Synonyms and Boolean operators were used to create search strings executed across Scopus, ScienceDirect, and EBSCOhost, following defined inclusion and exclusion criteria. Table 2 summarises the search strategy and results. A total of 604 publications were retrieved, of which 77 met the selection criteria and were analysed in detail. These studies provided the foundation for identifying and consolidating human-related factors, which were later examined through thematic and network analysis.

The combined database searches yielded 604 records, of which 116 duplicates were removed, leaving 488 unique studies for screening. Titles and abstracts were reviewed against the inclusion criteria, leading to the exclusion of 341 records that were not directly related to lean construction or human barriers. The remaining 147 articles were retrieved in full text and assessed for eligibility. Seventy of these were excluded because they either lacked methodological rigour, addressed technical scheduling models without human-related analysis, or were outside the construction sector. This process resulted in a final set of 77 studies that were included in the systematic review. The selection pathway is illustrated in the PRISMA flow diagram (Figure 1), which shows the progressive refinement of records across the identification, screening, and inclusion stages.

After screening, data from 77 selected studies were systematically extracted to identify human factors influencing lean construction scheduling. Bibliographic details such as author, year, and country were recorded in Microsoft Excel for reference management. Each article was reviewed in full, and relevant text relating to human or behavioural factors was highlighted using EndNote 21, as shown in Figure 2. The extracted data were organised into five columns, recording the factor, description, author's stance, page number, and article ID. This process produced 615-factor entries, forming a structured foundation for subsequent thematic and network analysis.

A “factor” was defined as any explicitly stated human-related barrier, enabler, or behavioural condition influencing lean construction or scheduling performance. During extraction, semantically similar expressions (e.g. “lack of training” and “insufficient lean education”) were recorded separately to preserve the original author's intent. Consolidation into 54 categories was conducted through iterative thematic comparison, in which conceptually overlapping items were merged based on their shared underlying meaning rather than on wording similarity. To enhance coding reliability, extracted items were re-reviewed and cross-checked against original sources to ensure conceptual consistency prior to aggregation.

The recorded data from 615 entries were organised and categorised through thematic analysis to identify recurring patterns among human aspects affecting lean construction scheduling. Each factor was coded and grouped into one of eight themes, with overlapping or duplicated terms standardised for consistency. This process refined the dataset to 54 unique factors, each assigned a thematic code for analysis. Relationships between factors were then examined to determine influence and polarity, defined as positive, negative, or neutral, as shown in Figure 3. These structured relationships served as the foundation for subsequent network and causal loop analyses.

The previously coded data, together with the developed author–factor matrix and interrelationship matrix, were used for network and system analysis. Vensim software was used to generate the causal loop diagram and calculate centrality, enabling visualisation of the dynamic relationships among factors. Gephi software was then used to compute eigenvector centrality, identifying the most influential nodes within the network. Together, these analytical tools provided a comprehensive understanding of how human factors interact and shape lean construction scheduling efficiency.

Expert interviews were conducted to complement the systematic literature review and provide contextual support for the findings within the New Zealand construction context. The aim was to confirm whether the most influential behavioural factors identified through the SLR align with industry practice. Five experts were purposively selected, each with over five years of experience and direct involvement in lean construction. Participants were recruited through academic and professional networks and provided with information on confidentiality. Interviews were conducted online via Microsoft Teams using a semi-structured format, lasting 20–40 minutes. All sessions were recorded with consent, transcribed, anonymised, and securely stored in AUT OneDrive. As presented in Table 3, the participants represented a balanced mix of academia and industry, including lecturers, researchers, engineers, and senior managers. Although the sample size was limited to five participants, purposive selection prioritised depth of lean construction experience over numerical breadth. The interviews provided contextual alignment with the literature-derived findings and helped assess their practical resonance within the New Zealand setting.

This section presents the analytical results from the systematic literature review and thematic coding of 54 unique human variables influencing lean construction scheduling. The analysis interprets structured data to examine interrelationships, frequencies, and influence pathways among factors. Using narrative interpretation, matrix mapping, and systems-thinking tools, it provides a comprehensive understanding of how human variables shape lean scheduling performance. The author–factor and interrelationship matrices, provided in the appendix 1 and 2, formed the analytical foundation. Frequency analysis identified dominant themes, while causal loop and centrality measures highlighted key influential factors. Eigenvector centrality further revealed systemic importance. Expert validation complements these findings with practical industry perspectives.

Figure 4 illustrates the annual distribution of publications between 2020 and 2025. In terms of overall contributions, the United States leads with 7 articles, followed by the United Kingdom and India with 6 each. Other regular contributors include Australia, China, Malaysia, and New Zealand. Countries with 1–2 publications were grouped under “Others,” which includes Brazil, Colombia, Egypt, Finland, Jordan, Qatar, Singapore, and Turkey, with 2 each, alongside 15 nations with 1 each, such as Canada, Chile, Ethiopia, Ghana, and Vietnam. This distribution highlights both the growing interest in lean construction research and its global diversity, though it is concentrated in a smaller number of leading countries.

This analysis examined how often each human factor appeared across the reviewed literature to highlight those most commonly recognised in lean construction research. Using the Author–Factor Matrix, 54 unique factors were evaluated across 77 studies and ranked by frequency. The most cited factor was Inadequate Conceptual Knowledge (A01), appearing in 42 studies, followed by Cultural Resistance to Change (O04, 31 mentions), Lower Motivation and Engagement (B05, 28 mentions), and Lack of Continuous Lean Training (T03, 28 mentions). These reflect recurring organisational, cultural, and skill-related barriers. At the lower end, factors such as CM05 and D03 were mentioned only once, though they may still hold contextual relevance as shown in Table 4 below.

A causal loop diagram (CLD) was developed using Vensim software to examine the systemic interactions among behavioural factors. As shown in Figure 5, the diagram illustrates how 54 generalised factors interact through 294 directional linkages, forming a dense network of reinforcing relationships. Based on these mapped relationships, the degree of centrality was calculated to determine the most structurally influential factors. Centrality measures the importance of each factor by assessing its number of incoming and outgoing links, revealing leverage points where interventions can create broader system-wide improvements (Bamakan et al., 2019). The results (Table 5) show “Lower Motivation and Engagement” (B05) and “Inadequate Top Management Support” (O03) as the most central, followed by “Cultural Resistance to Change” (O04) and related collaboration factors.

Network edges represent co-occurrence relationships identified within the reviewed studies, indicating conceptual association rather than causal dependency. Centrality measures were employed to assess structural prominence within this conceptual network, identifying factors occupying influential positions within the interconnected structure. While frequency reflects regularity in literature, centrality captures the quality of relationships, thereby reducing the risk of equating popularity with systemic influence.

Eigenvector centrality is a robust metric in network analysis that identifies the most influential nodes in a system by considering not just the number of connections or degree of a node, but also the importance of the nodes it is connected to (Bienenstock and. Unlike degree centrality, which treats all connections equally, eigenvector centrality gives more weight to links connected to already well-connected nodes.

Based on the interrelationship matrix developed in the previous section, eigenvector centrality values were calculated using Gephi 0.10, which is a specialised network visualisation and analysis tool (Link to the website). The output was normalised using the following formula:

The results were used to rank the most influential factors within the system, as shown in Table 6. These findings are visually represented in Figure 6, where the node size reflects the relative centrality score. The larger the node, the more structurally influential the factor is in the network.

To explore how human barriers interact systemically, a loop cycle analysis was performed using the causal loop diagram results. Unit weight measures the number of unique factors in each loop, while centrality weight reflects how structurally influential the loop is. The results, summarised in Table 7, show that Loops 1 and 2 contain the most factors (21 each), followed by Loops 3 and 4 (20 each). Loop 3 achieved the highest centrality score of 11.27, dominated by critical factors such as lower motivation (B05), weak management support (O03), and inadequate training (T03). High-weight loops demonstrate how inefficiencies reinforce each other, while lower-weight loops reveal localised behavioural issues and potential leverage points for intervention within lean construction systems.

As shown in Table 8, collaboration, leadership support, and motivation were the most frequently emphasised by participants. The other three factors, inadequate conceptual knowledge, conservative attitude, and cultural resistance to change, were also highlighted, though with slightly less frequency, confirming their ongoing significance in practice. These results indicate that the human barriers identified through global research are also visible in the New Zealand context.

This section interprets the findings by focusing on the human factors that most strongly influence inefficiencies in lean construction scheduling. Drawing on results from the systematic literature review, degree of centrality, interrelation mapping, and expert validation, the analysis identifies factors with both academic and practical significance in the New Zealand context. A comparative assessment was conducted using three analytical dimensions: SLR frequency, degree of centrality, and eigenvector centrality. This multi-layered approach highlights not only how frequently each factor appears in the literature but also its structural importance within the interconnected network of human barriers. Table 9 summarises the comparative results, showing the top-ranked factors across all three measures.

Six factors emerged as the most critical contributors to lean scheduling inefficiencies: lower motivation and engagement (B05), inadequate collaboration between stakeholders (C03), inadequate commitment and support from top management (O03), cultural resistance to change (O04), conservative and negative attitude (B03), and inadequate conceptual knowledge (A01). Their consistent appearance across multiple analyses indicates that inefficiencies in lean scheduling arise from complex human interactions rather than isolated issues. Together, these six factors provide the foundation for developing targeted strategies to enhance the application of lean in New Zealand.

The core network structure was derived from a global systematic literature review, indicating that the identified patterns reflect internationally recognised lean construction challenges. The New Zealand interviews provided contextual validation, suggesting that the findings are transferable to similar project-based construction markets.

Lower Motivation and Engagement (B05): Motivation is central to lean practices such as the Last Planner System (LPS), which depend on active participation. However, perceived complexity and meeting fatigue often lead to disengagement, weakening collaboration (Hua and Schwartz, 2021). Motivation drives proactive behaviours like risk anticipation and constraint management (Power et al., 2024). Strategies such as linking promotions or recognition to lean achievements can enhance engagement (Likita et al., 2025). As one expert noted, “The challenge is not only about the tools but about how motivated people are to use them” (P2). Therefore, sustaining motivation is crucial for transforming lean practices into a shared mindset that drives continuous improvement.

Inadequate Collaboration Between Stakeholders (C03): Effective collaboration among contractors, consultants, and suppliers underpins lean success. Poor collaboration disrupts workflows and reduces trust. Studies show that LPS enhances coordination (Bajjou and Chafi, 2022) and that interdepartmental cooperation improves project efficiency (Berawi et al., 2023). Conversely, fragmentation and misaligned goals increase inefficiencies (Hyarat et al., 2024). Experts reinforced that collaboration forms “the people side of lean pillars” (P1). In short, it acts as a systemic barrier that weakens trust, disrupts coordination, and prevents lean construction from achieving its full efficiency potential.

Inadequate Commitment and Support from Top Management (O03): Leadership commitment determines whether lean initiatives are sustained or superficial. Without consistent management support, motivation and accountability weaken (Hughes and Thorpe, 2014). Visible leadership engagement through resources, follow-ups, and cultural endorsement enhances adoption (Likita et al., 2024). Expert feedback confirmed that strong managerial backing is essential for maintaining site-level enthusiasm and embedding lean culture. Hence, it weakens the cultural and motivational foundations of lean, highlighting that sustained leadership commitment is crucial. These findings are also consistent with broader organisational and leadership research, which emphasises the central role of culture and managerial commitment in shaping behavioural change within complex project environments.

Cultural Resistance to Change (O04): Cultural inertia and adversarial traditions remain major barriers to lean transformation. Research across China, Turkey, and the Middle East links hierarchical and conservative work cultures to limited lean adoption (Bayhan et al., 2023; Ugurlu et al., 2021). Similar patterns exist in New Zealand, where ingrained habits and scepticism toward new methods continue to impede change. Therefore, cultural resistance reinforces traditional practices and obstructs innovation, and thereby acts as a major barrier to cultural transformation.

Conservative and Negative Attitude (B03): At the individual level, conservative mindsets restrict innovation and openness to new tools (Kirby et al., 2025). Negative perceptions of lean can lead to disengagement, preventing systems such as LPS from achieving their full benefits (Sarhan et al., 2019). Experts emphasised that entrenched attitudes among senior staff often slow progress. In short, this factor creates behavioural rigidity that limits adaptability, open-mindedness, and continuous learning within lean teams.

Inadequate Conceptual Knowledge (A01): A limited understanding of lean concepts reduces the effectiveness of implementation. Insufficient training creates confusion and resistance (De Silva et al., 2025; Hamzeh and Albanna, 2019). Knowledge-sharing across teams enhances collective capability (Chen et al., 2023; Malvik et al., 2024). Expert interviews confirmed ongoing conceptual gaps in New Zealand's workforce, underscoring the need for structured lean education. Therefore, this factor prevents teams from translating lean theory into effective practice, making targeted education and knowledge exchange essential.

Causal loop analysis reveals how behavioural dynamics interact systemically to influence lean construction scheduling. Reinforcing loops illustrate how behavioural barriers such as low motivation, poor communication, and resistance to change compound over time. Loops with the highest unit and centrality weights, particularly Loops 1 to 5, represent the most influential feedback cycles, involving interlinked factors that perpetuate inefficiencies. Six key factors recur across these loops: lower motivation (B05), limited management support (O03), cultural resistance (O04), poor collaboration (C03), conservative attitude (B03), and insufficient conceptual knowledge (A01).

Among them, Loops 1 and 3 are the most significant. Loop 1, with the highest unit weight, demonstrates how a lack of respect and trust (C01) triggers weak culture, poor collaboration, and limited training, leading to low motivation and poor decision-making. Loop 3, with the highest centrality weight, highlights how poor leadership, negative attitude, and weak communication reinforce each other. Both loops show that improving trust, leadership, and collaboration is crucial to breaking self-reinforcing behavioural patterns that sustain inefficiencies. Expert interviews confirmed this, noting that leadership commitment and cultural support are essential to overcoming resistance and promoting collaborative lean practices.

Loop 1:

C01 → O02 → O04 → C03 → O05 → T03 → A04 → CM05 → B01 → CM03 → D05 → O03 → D08 → B03 → B08 → B04 → B09 → C07 → C06 → CM01 → B05 → C01

Loop 3:

C01 → CM03 → D05 → O03 → D08 → B03 → B08 → C07 → C06 → CM01 → C03 → O05 → T03 → A01 → A06 → O02 → O04 → B09 → B04 → B05 → C01

The efficiency of lean construction scheduling is shaped by a wide range of human factors that extend beyond technical processes. This study found recurring challenges such as skill shortages, an ageing workforce, training issues, low motivation, cultural resistance, and weak collaboration. These issues disrupt workflow reliability, delay project delivery, and limit the expected benefits of lean practices. Their influence is systemic rather than isolated, affecting project performance at multiple stages.

A qualitative design was adopted, using a systematic literature review (SLR) and expert interviews to capture both global evidence and local perspectives. The SLR analysed 77 studies, identifying 615 factors that were refined into 54 categories. Network analysis revealed six factors as most critical: lower motivation and engagement (B05), inadequate collaboration between stakeholders (C03), inadequate commitment and support from top management (O03), cultural resistance to change (O04), conservative and negative attitude (B03), and inadequate conceptual knowledge (A01). Expert interviews confirmed their relevance and highlighted additional themes, including psychological safety and transformational leadership.

These results address the research objectives by identifying the most influential people-centric factors and proposing strategies to mitigate their impact. The study indicates that interventions should target structurally influential factors rather than isolated variables. Centrality analysis suggests that leadership commitment, motivation, collaboration, and lean literacy function as leverage points within the broader relational system. Addressing these high-centrality factors first may generate wider systemic improvement and reduce the risk of fragmented or superficial lean adoption.

This research uniquely combines a systematic literature review with systems thinking and network analysis to explore how human factors collectively influence lean construction scheduling. Unlike previous studies that treat these factors separately, it examines their systemic interconnections using causal loop diagrams and centrality measures. A key innovation is the use of eigenvector centrality to identify the most structurally influential factors within the network. By integrating frequency and relational influence, this study provides a holistic framework for understanding and improving lean practices in New Zealand's construction industry. The study is primarily a systems-oriented synthesis of global literature, supported by contextual expert perspectives.

The contribution of this study lies not in identifying new human factors, but in structuring established factors as an interconnected system and distinguishing between prevalence and structural influence. By applying network centrality analysis, the research advances lean construction knowledge beyond descriptive barrier lists and provides actionable prioritisation for managerial intervention.

The centrality analysis identifies six structurally influential factors that function as leverage points within the relational network. This suggests that leadership commitment, motivation, collaboration, and lean literacy should be prioritised as foundational conditions before tool-based optimisation. Such sequencing shifts lean implementation from a procedural focus to a behavioural systems perspective, reducing the risk of superficial adoption.

This study is grounded in socio-technical systems theory and systems thinking, which conceptualise project performance as emerging from interconnected social and technical elements. By applying network and causal loop analysis, it extends behavioural project management research through empirically mapping systemic human interdependencies in lean construction scheduling.

This study contributes to theory and practice by showing that inefficiencies in lean construction scheduling stem not only from operational issues but also from systemic behavioural interactions. Through thematic, causal loop, and network analyses, it demonstrates how factors such as resistance, disengagement, and fragmentation reinforce inefficiencies. The research links qualitative insights with network theory, offering a framework for analysing human factors within interconnected systems. Practically, six key factors, including low motivation, weak collaboration, poor leadership support, cultural resistance, conservative attitude, and limited knowledge, highlight where targeted interventions should focus to enhance collaboration, leadership, and learning, fostering sustainable lean practices in New Zealand construction.

This study offers valuable insights into the human factors affecting lean construction scheduling in New Zealand, but it has several limitations. The systematic literature review used only peer-reviewed sources, excluding grey literature that could add practical context. Synthesising global findings may have diluted local nuances, and the five expert interviews, though rich in insight, limited the diversity of industry perspectives. While the sample size was limited, future research may expand participant representation to strengthen empirical robustness and broader industry applicability. The study also focused mainly on human-related variables, omitting external influences such as financial pressures or supply chain issues. Future research should expand participation, explore themes such as psychological safety and leadership style, and apply mixed-methods approaches to enhance generalisability and practical relevance.

Future research may examine causal relationships among the identified factors to understand better how these dynamics evolve over time. Further studies could also test the framework's transferability to other project-based industries.

The authors acknowledge the valuable contributions of the five construction professionals who participated in the expert validation interviews. This study involved voluntary participation, and all interviewees provided informed consent before data collection. No personal or identifying information was obtained or included in the analysis. According to the AUT ethical review policy (exemption policy 6.7), this type of research was exempt from requiring formal ethics approval. The author also wishes to thank the academic supervisors and colleagues for their guidance and constructive feedback throughout this research.

The supplementary material for this article can be found online