Systematic Review of Service Quality Models in Construction

Abstract

The construction industry is undergoing a significant transformation due to the increasing influence of digital technology, sustainability requirements, and diverse stakeholder expectations, which highlights the need to update the existing service quality models accordingly. However, the traditional service quality models often fail to address these evolving demands comprehensively. This study systematically reviews 44 peer-reviewed articles to identify the key service quality dimensions and offer clear guidance for future research that can address the complexities of modern construction. The findings reveal that reliability, tangibles, and communication remain the most emphasized dimensions across the reviewed literature, whereas critical areas, such as digital integration, sustainability indicators, and service recovery, are significantly underexplored. This contrast explicitly links the limitations of the classic frameworks to these emerging demands, highlighting their difficulty in accommodating the industry’s growing reliance on real-time data, an environmentally friendly performance, and multi-stakeholder collaboration. Because the construction industry typically contributes 6–10 per cent of the national GDP and underpins wider economic development, inadequate service quality models can propagate cost overruns, productivity losses, and reputational damage across the economy; conversely, improved models enhance project efficiency, and thus support sustained economic growth. This review is limited by its reliance on the Scopus and Web of Science databases, which may exclude relevant regional or non-English studies. Furthermore, many reviewed articles are context-specific, potentially reducing the generalizability of the findings. Despite these limitations, this review offers an evidence-based framework that integrates advanced digital tools, sustainability measures, and diverse stakeholder perspectives. Future studies should demonstrate this framework’s efficacy and applicability in different circumstances.

1. Introduction

The construction industry, as highlighted by Johnson, et al. [1], plays a significant role in economic development and necessitates continuous improvement in service quality to meet client expectations and achieve project success. The foundational theories, including those by Lewis and Booms [2] and Grönroos [3], initially defined service quality in terms of both the technical outcomes and the functional processes underlying service delivery. Building on these early conceptualizations, service quality models evolved considerably with the introduction of frameworks like the GAP model by Parasuraman, et al. [4], which was later refined into the SERVQUAL model. This model identified five key dimensions—tangibles, reliability, responsiveness, assurance, and empathy—yet its reliance on subjective customer perceptions poses challenges in complex industries like construction, where varied timelines and diverse stakeholder involvement complicate direct application [5]. Recent studies have directly examined these challenges, finding that the subjective nature of client perceptions can introduce variability and reduce the reliability of service quality assessments in construction. For example, Dosumu and Aigbavboa [6] found significant gaps between clients’ expected and observed service qualities, with notable differences between public and private client groups. Likewise, Shehu and Shehu [7] emphasize that construction project quality is fundamentally judged through stakeholders’ subjective perceptions, which may limit the accuracy and transferability of models like SERVQUAL in practice.

Efforts to tailor such models to construction began by adapting SERVQUAL and related frameworks, recognizing the industry’s unique demands: varied contract structures, multi-phase projects, and the interaction between technical and relational factors [8,9,10]. Performance-driven adaptations like SERVPERF addressed niche contexts such as small building maintenance, while advanced tools like Quality Function Deployment aligned contractor services with complex client needs [11,12]. Other researchers also explored specialized domains—facility maintenance [13], housing satisfaction [14], and engineering consulting [15]—to capture the nuanced service quality drivers in specific construction settings. These incremental refinements reveal how context-centric studies [6,16,17] enrich our understanding of service quality, but underscore the challenges of applying localized findings globally, while many conventional service quality models have yet to fully integrate new dimensions arising from digital transformation—including Building Information Modelling (BIM), real-time IoT data, and predictive analytics—which now shape project execution and stakeholder collaboration [18,19]. Cui, et al. [20] highlights how knowledge management critically shapes whole-process consulting quality, while Rahaman, et al. [21] notes that big data can enhance project decisions, yet faces data-integration challenges. Parekh and Mitchell [22] find AI-driven solutions improve on-site efficiency, but are constrained by talent shortages. Meanwhile, Win, et al. [23] and Setijanto, et al. [24] integrate brand image and relationship into service quality dimensions, illustrating that client satisfaction hinges not only on construction quality, but also on relational factors and co-creation.

Similarly, sustainability imperatives gain urgency as clients and regulators demand environmentally responsible and socially conscious outcomes [25,26]. While a handful of studies incorporate digital technologies or the sustainability dimension, these efforts often remain isolated or lack an overarching framework to assess how such innovations intersect with classical quality constructs like reliability, responsiveness, and assurance. Furthermore, although prior reviews Landy, Sousa and Romero [16],Love and Holt [27] establish a foundation for understanding construction service quality, recent studies show that modern service quality models—particularly those involving BIM integration, stakeholder engagement, and sustainability-driven practices—can enhance project efficiency, reduce waste, and improve client satisfaction, thereby strengthening the industry’s economic contribution [28,29,30]. These models move beyond technical delivery to address their long-term value and system-level performance.

IoT sensors integrated with BIM models enable the continuous, real-time monitoring of construction activities, facilitating prompt defect detection and more data-driven quality control decisions on site [31]. Additionally, AI-powered analytics and digital twin simulations improve inspection accuracy by predicting potential quality issues and minimising human error [32]. These digital advancements have made quality assurance more proactive and reliable; issues are identified and addressed earlier, service delivery more consistently meets the required standards, and overall client satisfaction is enhanced. However, there is not much research that shows guidance on how these dimensions must evolve to incorporate rapid digitalization, sustainability targets, and increasingly complex project structures. Consequently, while the existing literature has paved important groundwork, it has yet to fully synthesize these emerging challenges into a unified and adaptable model. This gap highlights the need for future research to integrate digital and sustainability considerations into established quality frameworks. To bridge this gap, this paper offers a holistic and integrative review that not only maps the dominant service quality constructs across industries and regions, but also explicitly interrogates how the emerging drivers—digital innovation, sustainability, and stakeholder plurality—reshape the expectations and limitations of the traditional models. Through synthesising 44 peer-reviewed studies, this review proposes guidance for developing more adaptive, inclusive, and context-sensitive service quality frameworks that better reflect the realities of the modern construction practice industry.

2. Theoretical Framework

2.1. Traditional Service Quality Models

The success of an organization is significantly influenced by service quality, which varies based on industry characteristics and client expectations. As noted by Lewis and Booms [2], service quality serves as a critical measure of how well services align with customer expectations, underscoring the need to consistently meet or exceed these expectations. Foundational models such as [3] the Technical and Functional Quality Model emphasize the dual importance of service outcomes and delivery processes in shaping customer perceptions. Expanding on this, Gummesson and Grönroos [33] introduce the 4Q model, incorporating design, production, delivery, and relational dimensions to address the multifaceted nature of quality.

Subsequent models further refined these concepts. Lehtinen and Lehtinen [34] highlights physical, corporate, and interactive quality dimensions, while Parasuraman, Zeithaml and Berry [4] developed the GAP model, later streamlined into the widely adopted SERVQUAL framework, focusing on tangibles, reliability, responsiveness, assurance, and empathy. Although influential, SERVQUAL faces criticism for its reliance on subjective customer perceptions.

Later models, such as Haywood-Farmer’s (1988) Attribute Service Quality Model [35] and Cronin Jr and Taylor [36]’s SERVPERF model, offered alternative perspectives, emphasizing service attributes and performance-based evaluations, respectively. Similarly, frameworks like the PCP Attribute Model [37] and Mattsson’s Ideal Value Model [38] introduced new approaches to prioritize service improvements. Teas [39] introduced the Evaluated Performance and Normed Quality Models, providing alternative frameworks for assessing performance. The Pivotal–Core–Peripheral (PCP) Attribute Model and the Retail Service Quality and Perceived Value Model offered structured ways to prioritize service improvements [40]. Collectively, these models laid the groundwork for adapting traditional frameworks like SERVQUAL and SERVPERF to the unique needs of construction and other complex industries, as will be discussed in the following sections. Research continues to build upon these foundational models, extending them to suit the context-specific needs of various construction industries.

2.2. Service Quality Models in Construction

Building upon foundational service quality models, significant progress has been made in the construction and real estate industries since the 1980s. Johnson, Dotsm and Dunlap [1] laid the groundwork by identifying the factors influencing service quality in real estate brokerage, while Asahara [41] expanded the application of the GAP model to construction industry, emphasizing the key factors across project phases. However, Asahara’s focus on smaller-scale projects raised questions about the applicability of the GAP model to larger and more complex projects. Samson and Parker [42] and Hoxley [43] applied the SERVQUAL model to assess service quality in building surveying services, yet these studies do not fully account for the complexities of construction services, such as subcontractor involvement and variable project timelines.

Nelson and Nelson [44] contributed the RESERV instrument for real estate brokerage quality assessment, providing insights despite limitations relate to geographic scope and sample size. Gable [45] introduced the Priori model for client–consultant interactions, but they did not address the diverse environments of construction projects. Winch, Usmani and Edkins [5] incorporated Business Process Re-engineering (BPR) and gap analysis into construction management, bringing a fresh perspective to service delivery improvement. Despite the potential of BPR for optimizing processes, its application to complex, large-scale projects lack sufficient empirical evidence. Browne and O’Donnabhain [46] explored the client–project manager relationship with the ‘Expectations-Artefact Model’ for ongoing client satisfaction evaluation.

The early 2000s marked a shift towards identifying the key service quality determinants and developing robust assessment tools. Hoxley [47] developed a 26-item scale for evaluating service quality among UK construction professionals, which, while comprehensive, poses challenges in smaller or resource-constrained projects. This model’s UK-centric development also limits its generalizability. Torbica and Stroh [48] introduced HOMBSAT for homebuyer satisfaction, highlighting dimensions such as access, communication, and reliability. Holm [49] highlights the importance of communication and quality control in housing refurbishment.

In studies focusing on integrating new dimensions and strategic approaches, Forsythe [50] investigated customer satisfaction in Australian housing projects, noting the impact of service incidents and pre-purchase expectations. Although rich in qualitative insights, this study is limited in scope. Durdyev, et al. [51] employed PLS-SEM to create a client satisfaction model in Cambodia, identifying the key service quality drivers relevant to future behavioural intentions. Prakash and Phadtare [52] explored service quality among architects in India, highlighting design quality, project administration, and relationship management. Their verified scale offers a detailed comprehension of service performance customised to customer requirements. Recent research has enhanced these models by integrating sophisticated approaches for regional or industry-specific variations. The work of Dosumu and Aigbavboa [6] used SERVQUAL to assess consultancy services in Lagos, underlining gaps in client engagement, but excluding contractor and regulator perspectives. For example, Landy, Sousa and Romero [16] employed PLS-SEM to highlight the importance of reliability and responsiveness in Cambodia, while Wang and Lim [17] identified dimensions such as operation management, health and safety, and communication in the Chinese construction industry. Muhammad, et al. [53] explored customer satisfaction in Ethiopian housing projects, focusing on design–build processes. Nevertheless, these efforts often face constraints, like small samples and narrow cultural contexts [54], showing the need for more generalized or adaptable frameworks.

2.3. Limitations of Service Quality Models in the Face of Emerging Trends

Chen, et al. [55] highlight significant gaps in digital competence among AEC practitioners, emphasizing the urgent need to address digital transformation and foster collaboration across diverse organizational types to meet the complex demands of modern construction projects.

Despite the evolution of these service quality models, most still focus on the conventional dimensions (e.g., reliability and responsiveness) and relatively linear client–contractor relationships, offering only limited consideration of modern challenges, such as digital transformation, sustainability imperatives, and the increasingly multi-stakeholder nature of construction projects [56]. Although, few researchers have investigated how to adopt digital technology in construction satisfaction, like Tripathi, et al. [57] utilized the Fuzzy Preference Relation technique to evaluate the performance factors influencing construction satisfaction. Soewin and Chinda [58] adopted a System Dynamics modelling approach to analyse and captured the causal relationships between client satisfaction and other performance factors, such as time, cost, quality, safety, and internal stakeholder involvement.

For instance, digital tools like Building Information Modelling (BIM) and the Internet of Things (IoT) enable the real-time tracking of construction progress, predictive maintenance, and data-driven decision making [18,19]. Other studies have shown that BIM-based platforms can streamline communication between contractors and clients, reducing rework and enhancing service outcomes [59,60]. However, these technologies introduce additional complexity to service quality evaluation, requiring dimensions that capture data interoperability, technical proficiency, and instantaneous responsiveness.

Alongside digitalization, sustainability is highlighted as a critical dimension in construction project [25]. Stanitsas and Kirytopoulos [61] identified and ranked the key sustainability indicators for construction project management, emphasizing environmental factors, and indirectly suggests that integrating these indicators could enhance stakeholder satisfaction by aligning project outcomes with modern sustainability expectations. However, the traditional service quality frameworks rarely integrate these environmental or social performance indicators, despite these factors being increasingly important to stakeholders, ranging from local communities to regulatory bodies [62]. Hence, service quality assessment must evolve to account not only for functional and technical performances, but also for how effectively a project aligns with broader societal and environmental responsibilities.

Without addressing such collaborative complexities, the traditional models may fall short in capturing how real-world projects distribute responsibility, balance competing objectives, and measure satisfaction across diverse actors.

3. Methodology

This study aims to synthesise and compare the primary evidence, provide an overview of the field’s development over time, and reconceptualise the service quality model for construction projects. This systematic review was conducted in accordance with the PRISMA guidelines. Given the conceptual diversity of service quality models, qualitative synthesis was performed in place of meta-analysis. An integrated literature review was conducted, as Sauer and Seuring [63] recommend that a systematic literature review aims to achieve the more comprehensive and cohesive use of information [64].

Sauer and Seuring [63] propose a six-step process for conducting a systematic literature review, which includes defining the research question, identifying the required characteristics of the primary studies, retrieving and selecting relevant literature, synthesising the findings, and reporting the results.

Figure 1 shows the six systematic literature review steps [63]. This research focuses on two main questions: “How does service quality (SQ) apply to construction projects?” and “How have assessment methods or service quality models in the construction industry evolved during digital transformation?” These questions guide analysis by contextualising this study. This research uses the inductive theory. We identified the primary research requirements and gathered the relevant literature. This review covers peer-reviewed articles and conference papers on the prior and current use of service quality models in construction projects. This review considers digital transformation and construction project stages to assess service quality. This review also considers digital transformation and construction project stage factors. The keywords for searching databases include combinations of “Service Quality” AND “Construction”. Scopus and the Web of Science provide extensive scientific literature databases for the literature search. Scopus and the Web of Science were chosen because together they offer the broadest peer-reviewed coverage of construction management journals and conference proceedings and provide robust citation-tracking tools [65]. By contrast, databases like Google Scholar and certain EBSCO collections are often excluded either because they significantly overlap with what Scopus/WoS covers without adding unique value [65]. Database selection can eliminate low-quality evidence, negatively impacting the review findings [63]. After collecting the literature, we used EndNote version 20 to standardize references and removes duplicates from the two databases to simplify the reference list. This study exported the data to Excel for classification and synthesis. To accommodate the variability of research designs, we recorded each study’s methodological approach (quantitative, qualitative, or mixed methods) in an Excel spreadsheet and employed descriptive coding, complemented by Python 3.12.3-driven network analysis, to identify convergent and divergent patterns across the evidence base. In order to validate the selected sources, this study established explicit inclusion criteria based on Keele [66]’s quality assessment framework, focusing on works with clear research objectives, a robust methodology, and relevance to service quality in the construction industry. Finally, Python network analysis revealed construction service quality relationships, supporting this integrative review. The Python network visualisations exposed broad co-occurrence patterns between core service quality dimensions, and the main construction project types. These high-level relational maps then guided the thematic grouping of studies, ensuring that the narrative synthesis captured both commonalities and industry-specific nuances [67].

The initial search of Scopus and the Web of Science yielded 13,565 records on service quality across health, banking, transport, IT services, hospitality, and other industries. Because this review examines how service quality is conceptualised and measured specifically within the construction industry, we employed the Boolean string “Service Quality AND Construction Project NOT Health NOT Hospitality” and related filters. These automated exclusions—together with the removal of papers that dealt primarily with Total Quality Management—eliminated 8936 ineligible items before any manual screening. Consequently, 4629 records entered the title, abstract, and keyword screening stage, where a further 4551 were excluded for lacking a direct construction focus. The remaining 78 full-text articles were retrieved successfully and assessed against explicit inclusion. Of these, 44 studies met all the inclusion criteria—including a clear focus on construction service quality, explicit research questions, and methodological transparency—and were included in the final review. To ensure replicability, Carter and Washispack [68] suggest selecting these papers again based on the full text. The study selection process is detailed in the PRISMA flow diagram (Figure A1).

In order to validate the selected sources, this study established explicit inclusion criteria rooted in the overarching research focus. Drawing on Keele [66]’s quality assessment framework, the key benchmarks included a clear statement of research objectives or questions, a robust description of the methodology, and thorough presentation and discussion of the findings. In addition, following guidance from Wawak, et al. [69], the final selection was narrowed to include works that directly address the research questions, focus on service quality in the construction industry, and examine service quality factors or models specific to construction projects.

Finally, forty-four papers remained based on the inclusion and exclusion criteria. The guidance provided by Sauer and Seuring [63] mentions that the number of papers that justify a literature review can be defined based on the existing example from a previous literature review. Based on research Landy, Sousa and Romero [16], this paper has the same scope of service quality, but applied in different industries. In total, 22 papers were selected in Azzari, et al. [70]’s research with an accounting background, 33 papers were reviewed by Prakash and Phadtare [52] within the construction industry, and 33 papers were selected based on research from Landy, Sousa and Romero [16], which also investigates service quality in the construction industry. Accordingly, we retained only those studies that explicitly examine service quality constructs or models within the construction industry and that directly relate to our research questions, thereby securing a sample that is both highly pertinent and representative. Thus, this research collects 44 papers, which is a reasonable number, particularly for service quality models in the construction industry. No new empirical data were gathered for this review; instead, the empirical results presented in the 44 peer-reviewed articles and conference papers constitute the primary evidence base that was synthesised.

4. Results

The previous studies collectively advanced the understanding of service quality by applying diverse methodologies across different segments of the construction industry. To capture these diverse perspectives, we performed a thorough literature search in Scopus and the Web of Science. We followed the guidelines from Keele [66] to narrow down the huge number of papers. This research only included those that explicitly address service quality factors or models in the construction industry and that offer direct insights for our research questions. The studies were included only if they presented empirical findings, conceptual frameworks, or methodological discussions that directly inform how service quality is conceptualised, measured, or improved in construction settings.

Table 1. Representative studies on service quality in construction.

Author and Year	Main Limitations	Main Advantages
Muhammad, Soepatini and Isa [53]	Limited to Surakarta; findings may not generalize to other regions.	Provides insights into customer satisfaction in design–build projects; highlights key risk analysis and service dimension preferences.
Win, Dodanwala and Santoso [23]	Limited to Myanmar; cross-sectional study; no long-term trend analysis.	Develops an integrated SEM model linking service quality, brand image, satisfaction, and loyalty.
Setijanto, So, Alamsjah and Tjhin [24]	Focuses on Indonesia; cross-sectional survey; lacks additional predictors like satisfaction.	Validates key factors influencing developer loyalty in apartment construction; emphasizes corporate image and co-creation.
Oyeyipo, Adeyemi, Osuizugbo and Ojelabi [56]	Limited to Lagos, Nigeria; findings may not generalize widely.	Identifies service quality gaps in building services consultancy; suggests practical improvements for client satisfaction.
Ngowtanasuwan [71]	Only three competency factors examined; broader scope needed.	Defines competency factors for Thai SME contractors; valuable framework for performance assessment.
Giao and Trang [72]	Limited to construction project management; broader service industries not analysed.	Proposes construction-specific SERVQUAL-based dimensions for better quality measurement.
Landy, Sousa and Romero [16]	Limited to Cambodia; cultural specificity affects generalizability.	Comprehensive PLS-SEM model analysing service quality effects on client satisfaction in construction.
Ngowtanasuwan and Iop [54]	Limited to Thai provincial contractors; further validation needed.	Identifies empathy, responsiveness, and tangibles as critical service quality factors.
Wang and Lim [17]	Small sample (90 responses); does not support initial hypothesis fully.	Analyses contractor service quality through operation, safety, and communication dimensions.
[6]	Focuses only on clients; lacks contractor/regulatory perspectives.	Thorough SERVQUAL-based evaluation of construction consultancy service quality.
Durdyev, Ihtiyar, Banaitis and Thurnell [51]	Limited to Cambodia; language nuances may affect results.	Validates SERVQUAL dimensions and behavioural intentions using PLS-SEM.
Prakash and Phadtare [52]	India-specific scale; snowball sampling may limit representativeness.	Develops an empirical service quality scale for architects, improving business performance insights.
Hadidi, et al. [73]	Focuses on Saudi Arabia; limited to engineering design services.	Examines impact of ISO 9001 certification on customer satisfaction in construction.
Chen, Yan, Yang, Bian and Chi [15]	Provides a practical, multi-dimensional model for improving ECS quality, with actionable recommendations for consulting firms.	Limited generalizability due to cultural differences and lacks a dynamic approach to measuring service quality.
Forsythe [74]	Single case study; may not generalize to other markets.	Explores service quality impact on customer satisfaction in Australian detached housing projects.
Eldejany [75]	Limited to Australian small construction businesses.	Analyses how reliability, assurance, and empathy affect satisfaction and repurchase intentions.

showcases the core set of studies identified through this process. Following duplicate removal, title/abstract screening, and full-text appraisal against our inclusion criteria, 44 studies were retained. Table 1 showcases the core set of studies identified through this process, which collectively highlight the dominance of SERVQUAL constructs, methodological preference for PLS-SEM, and a prevailing geographic focus on the Asia–Pacific region.

From Table 1, the results show that many studies adapt robust models like SERVQUAL [6,72] and employ advanced methods, such as SEM and PLS-SEM. This supports the argument that service quality connects to satisfaction [53,74]. SERVQUAL offers a structured, multi-dimensional diagnostic framework that disaggregates service quality into specific dimensions (e.g., tangibility, reliability, responsiveness, assurance, and empathy), enabling firms to pinpoint the performance gaps in each area [76]. SEM (including its PLS-SEM variant) facilitates the rigorous validation of these latent constructs and simultaneously models their complex inter-relationships, for example by linking the service quality dimensions to client satisfaction and loyalty, thereby strengthening the reliability and explanatory power of empirical analyses [51]. Some authors propose industry-specific frameworks [52,54]. Others explore emerging solutions like AI-driven risk management [22]. Collectively, they show that better service quality can boost loyalty, reduce risk, and help projects succeed. However, the results also indicate that researchers often rely on single geographic or cultural contexts. This choice may be convenient, but it also limits how much their findings can be generalised. For instance, Muhammad, Soepatini and Isa [53] focus only on Surakarta, Oyeyipo, Adeyemi, Osuizugbo and Ojelabi [56] on Lagos’, and Durdyev, Ihtiyar, Banaitis and Thurnell [51] on Cambodia. Other studies use small, localised samples (Wang and Lim [17]) or highlight only one stakeholder’s viewpoint [6]. This approach is understandable because collecting data across multiple settings is difficult. But the results are usually of limited relevance, often missing long-term or cross-cultural trends [23].

Table 1 also indicates that reliance on single-city or single-culture data renders many of these contributions’ context-bound, and thus potentially misleading when applied elsewhere. For example, Muhammad [53]’s design–build analysis in Surakarta, Indonesia, found responsiveness to be the primary driver of client satisfaction, whereas Oyeyipo, Adeyemi, Osuizugbo and Ojelabi [56]’s Lagos-based study identified reliability as the most salient dimension. These contrasting patterns demonstrate that the findings derived from a single geography should be extrapolated with caution; robust cross-cultural validation is essential before universal prescriptions can be drawn.

Cultural factors distinctly shape construction service quality. First, national contract regimes vary, for example, the collaborative ethos of NEC/JCT contracts (common in the UK) vs. the detailed FIDIC forms or simple lump-sum agreements, reflecting distinct legal–cultural norms that shape how contracts are executed [77]. Second, the communication style differs. High-context cultures (e.g., East Asia) rely on implicit cues, which may hinder clarity in low-context (Western) settings and affect project coordination and quality control [78]. Third, responsibility structures also diverge; some contexts favour architect-led delivery, whereas others use EPC contractor-led models, introducing professional culture gaps in accountability for quality outcomes [79]. Finally, housing practices such as off-plan sales (pre-completion purchases) are routine in markets like China and Australia but less so in the UK, a divergence that influences buyer expectations and regulatory oversight, thereby impacting construction quality performance [80]. The key patterns and thematic insights summarised in Table 1 are further explored and visualised in the next section. The details of relevant studies reviewed in this section are summarized in Appendix A (

Table A1. Selected studies on service quality in construction.

Author and Year	Contribution	Limitations
Muhammad, Soepatini and Isa [53]	Provides insights into how customer expectations and satisfaction form in Design Build projects in Surakarta, deepening understanding of consumer decision making.	Its qualitative, geographically limited approach and lack of quantitative causal testing reduce broader applicability.
Win, Dodanwala and Santoso [23]	Develops and validates an integrated model linking service quality, brand image, customer satisfaction, and loyalty in Myanmar’s construction industry, revealing key mediation effects.	Reliance on cross-sectional data from Myanmar limits causal conclusions and generalizability to other contexts.
Setijanto, So, Alamsjah and Tjhin [24]	Shows that service quality, Guanxi, value co-creation, and corporate image positively affect customer loyalty in apartment construction, offering actionable managerial insights.	Focused solely on Indonesian apartment projects and excludes variables like customer satisfaction, limiting comprehensiveness and generalizability.
Oyeyipo, Adeyemi, Osuizugbo and Ojelabi [56]	Uses the SERVQUAL model to identify expectation perception gaps in building services consultancy from Nigerian clients’ viewpoints.	Study confined to Lagos, which may not represent the entire Nigerian construction industry.
Ngowtanasuwan [71]	Provides guidelines for Thai SME contractors by identifying 12 nuanced service quality gaps, including discrepancies in owner expectations.	Focuses on only three competency factors, possibly omitting others needed for a fuller picture.
Landy, Sousa and Romero [16]	Explores how service quality influences client satisfaction and future behavioural intentions in construction within developing countries.	Findings are context-specific (e.g., Cambodia), limiting the applicability to other regions or larger projects.
Ngowtanasuwan and Iop [54]	Identifies empathy, responsiveness, and tangibles as key service quality factors and offers practical strategies for boosting owner satisfaction.	Limited geographic focus and small sample size affect its generalizability.
Wang and Lim [17]	Proposes a resource based view framework for perceived service quality in construction firms.	A small sample from Zhejiang and a failure to confirm all hypothesised dimensions may omit critical factors.
Dosumu and Aigbavboa [6]	Evaluates construction consultancy service quality using a 20-item SERVQUAL model and offers specific improvement recommendations.	Focuses on small/medium projects and client perspectives only, neglecting views from other stakeholders.
Durdyev, Ihtiyar, Banaitis and Thurnell [51]	Examines the impact of service quality on client satisfaction and future intentions in Cambodian construction projects.	Limited to small/medium projects in Cambodia with potential cultural and translation nuances.
Prakash and Phadtare [52]	Develops a validated scale to measure service quality for architects in India.	Context-specific scale and use of snowball sampling may limit representativeness.
Hadidi, Assaf, Aluwfi and Akrawi [73]	Assesses the effect of ISO 9001 certification on enhancing customer satisfaction in construction design management services.	Restricted to Saudi Arabian engineering design services, limiting broader applicability.
Chen, Yan, Yang, Bian and Chi [15]	Proposes a multi-level, multi-dimensional model for evaluating ECS quality in construction.	Cultural differences and a static approach limit adaptability; dynamic measurement is not fully addressed.
Eldejany [75]	Explores relationships between service quality, customer satisfaction, and repurchase intentions in Australian building maintenance.	Findings are specific to the Australian residential market and may not extend elsewhere.
Forsythe [74]	Provides a real time view of how daily onsite service incidents influence customer satisfaction in housing construction.	Based on one detailed case study, limiting its broader applicability.
Forsythe [125]	Advances theory by identifying testable variables affecting the dynamic link between service quality and customer satisfaction.	Practical application is challenging due to dependence on ongoing customer perception management.
Sunindijo, Hadikusumo and Phangchunun [10]	Empirically demonstrates how service quality influences client satisfaction and behavioural intentions, with satisfaction mediating loyalty.	Results may not extend beyond the Thai construction industry due to non-probabilistic sampling.
You, et al. [126]	Clarifies factors influencing service quality in real estate brokerage from a construction firm’s perspective.	Taiwan-based data and a complex integrated model may limit practical application elsewhere.
Lai and Lai [127]	Assesses maintenance contractors’ service quality in public housing based on tenant expectations and perceptions.	Limited to a specific tenant demographic, possibly overlooking broader perspectives.
Forsythe [50]	Offers insights into pre-purchase expectations that can help contractors improve customer satisfaction.	May not fully capture the diversity of customer expectations across different groups.
Araloyin and Olatoye [128]	Analyses real estate consumers’ service quality perceptions in Lagos to inform improved service delivery.	Focused on Lagos, so findings may not be applicable to other regions.
Bjeković and Kubicki [129]	Proposes a model integrating business and technical perspectives to enhance service design in the AEC industry.	Early stage model with uneven detail and difficulty distinguishing quality attributes.
Kärnä, et al. [130]	Offers strategies for enhancing customer satisfaction through better service quality, communication, and process management in construction.	Recommendations may oversimplify complex industry issues and not address underlying systemic challenges.
Tuzovic [131]	Compares perceptions of virtual versus physical service encounters among real estate buyers and renters.	Limited to higher-education professionals, affecting generalizability across demographics.
Juan [132]	Proposes a decision support approach using case based reasoning and DEA to address refurbishment information asymmetry.	Does not capture all potential factors (e.g., cultural, economic) affecting consumer satisfaction.
Forsythe [133]	Develops an interdisciplinary model for creating customer profiles to help housing contractors improve management and leverage service quality competitively.	Uncertain dimension weightings and potential need for adaptation limit its precision and broader applicability.
Marja Rasila and Florian Gersberg [13]	Evaluates outsourced facility maintenance service quality from an end-user perspective.	Findings based on limited case study interviews may not generalize widely.
Forsythe [134]	Introduces the BUILDSERV instrument to convert abstract service quality concepts into practical measures for housing construction.	Instrument requires further refinement and broader validation.
Cheng, et al. [135]	Investigates performance of construction consultants to determine key performance attributes impacting client satisfaction.	Data from a single large consultancy may not reflect the broader industry.
Dabholkar and Overby [118]	Establishes actionable links among service quality, customer satisfaction, service process, and outcomes.	Limited by single decision-maker input and a small sample, hindering extreme case analysis.
Ling and Chong [83]	Examines service quality of design and build contractors in Singapore’s public industry projects.	Small sample size and subjective client evaluations limit its broader applicability.
Arditi and Lee [12]	Introduces a QFD based tool to measure quality performance of design build contractors based on owner expectations.	Tailored for D/B firms and does not address quality variations across different project stages.
Maloney [136]	Identifies key factors (relationship, management, safety, workforce, cost-effectiveness) that influence client satisfaction and contractor selection.	Lacks empirical validation and specific procedures for measuring and improving service quality.
Siu, Bridge and Skitmore [9]	Finds that service performance often falls short of client expectations and recommends closer client engagement.	Small sample and potential omission of key dimensions (e.g., safety, sustainability) may limit broader relevance.
Al-Momani [92]	Advocates integrating Business Process Re-engineering and gap analysis in construction management to improve quality standards.	Insufficient focus on owner satisfaction, limited gap interpretation, geographic bias (e.g., Jordan), and lack of detailed implementation plans.
Hoxley [47]	Develops a 26-item scale for assessing service quality in UK construction professional services with proven reliability.	Requires large samples and complex data processing, increasing cost and difficulty.
Holm [88]	Explores the relationship between service quality and tenant satisfaction in residential retrofits, emphasizing communication and quality control.	Limited sample size and geographic/cultural diversity reduce generalizability; implementation strategies need further refinement.
Torbica and Stroh [48]	Proposes an instrument to measure home-buyer satisfaction in construction projects.	May not fully account for external factors (e.g., income, demographics) and requires a larger sample for robust validation.
Winch, Usmani and Edkins [5]	Presents a comprehensive gap analysis model that integrates service quality, design review, and client involvement to minimize expectation performance gaps.	Practical application of gap analysis and BPR remains largely untested and its generalizability is uncertain.
Nelson and Nelson [44]	Develops a tailored service quality instrument for real estate brokerage and consulting, identifying key dimensions and offering practical recommendations.	Small sample size, fixed geographic scope, and focus solely on home sellers limit overall applicability.
Samson and Parker [42]	Adapts the SERVQUAL GAP model for Australia’s consulting engineering industry to systematically measure expectation service discrepancies.	Extensive modifications to SERVQUAL may restrict its applicability to other service industries.
Hoxley [43]	Introduces the SURVEYQUAL model for building surveying services, emphasizing the critical role of responsiveness.	Context-specific to UK building surveying and based on subjective perception data, which may limit generalizability.
Asahara [41]	Demonstrates the applicability of the GAP model in construction by categorizing determinants into ability and attitude related factors.	Constrained by project scale and neglects sustainability considerations.
Johnson, Dotsm and Dunlap [1]	Analyses behavioural determinants of service quality in construction contexts.	Salesperson interviews may suffer from social desirability bias, affecting result validity.

).

5. Findings

5.1. Service Quality Dimensions Network and Industry Frequency Distribution

This section finds the key service quality dimensions across various aspects of the construction industry, highlighting their specific priorities and the impact on client satisfaction and project outcomes across diverse industries.

Figure 2 illustrates the key service quality dimensions emphasised in this network, including reliability, responsiveness, assurance, and effective communication, echoing those identified as critical in the construction industry literature research [16]. The interconnections among these dimensions suggest they seldom operate in isolation; rather, a good performance in one aspect often complements or reinforces others as a part of an integrated service delivery approach. This pattern indicates that the construction industry as a whole adopts a holistic and adaptive strategy to service quality, wherein firms concurrently address multiple quality dimensions to meet client expectations in dynamic project environments. In effect, the network’s clustered links reveal overarching service delivery patterns and strategic priorities that transcend individual project types, signalling that the core aspects of service excellence (e.g., consistent reliability paired with strong client communication) are universally valued and pursued across the industry.

Different construction industries prioritise distinct service quality dimensions. Contractor services concentrate on reliability in delivering tangible results and on maintaining cooperative client relationships (analysing customer satisfaction and quality in construction, which is the case of public and private customers). Real estate brokerage, being client-facing, relies heavily on communication, responsiveness, and courtesy to build client trust [81]. Design–build firms focus on providing high design quality tailored to client needs, while ensuring timely project completion [82,83]. In housing construction, reliable and responsive service during the build phase is critical for customer satisfaction [74].

Real estate sales and facility management (FM) are both pivotal to maximising a building’s lifecycle value, from initial concept to long-term operation. In markets such as the UK, Australia, and China, developers commonly rely on off-plan sales (pre-completion contracts) to secure financing and gauge demand, which makes pre-sale marketing teams influential stakeholders in early design and investment decisions [84,85]. By contrast, in the US and much of Europe, property units are usually sold only after completion (often through brokers), reducing the relevance of integrating a pre-sale function into the project’s development phase [86].

Likewise, integrating FM expertise into the project lifecycle is critical for a good performance and value delivery over time. Some studies show that the early involvement of FM specialists during design and construction leads to fewer maintenance defects and more efficient building operations, enhancing the long-term asset performance [87].

Facility maintenance services require prompt service recovery and consistent reliability to minimise downtime [9]. Refurbishment projects hinge on meeting client expectations through effective communication and satisfaction management [88]. Similarly, small- and medium-sized construction projects rely on a dependable, responsive service (with assured professionalism) to satisfy clients [10]. Figure 3 shows the regional variation in the construction industry focus across categories, such as Housing Construction, Contractor Services, and Real Estate Brokerage. This indicates that certain industries are more prominent in specific regions (e.g., housing in Australia and brokerage in the US).

This pattern aligns with underlying market conditions and policies in those regions. In Australia, the pronounced emphasis on housing construction likely reflects the nation’s acute housing demand and affordability challenges, which have made residential building a central priority [89]. The “Housing Construction” industry is most prominently represented in Australia, indicating a high level of research focus on this industry within the region. By contrast, the United States’ high frequency of real estate brokerage corresponds to its mature, broker-centric property market, where formal licencing and industry structure give brokers a pivotal role in transactions [90]. Conversely, “Contractor Services” and “Facility Maintenance” are more distributed across different regions, such as Nigeria, Japan, and Saudi Arabia, suggesting a broader interest in these service aspects in various contexts. The European cases, meanwhile, tend to highlight industries such as refurbishment and facility maintenance, consistent with Europe’s aging building stock and stringent energy efficiency regulations that prioritise upgrading existing structures [91]. The representation of “Refurbishment Projects” and “Small and Medium Construction Projects” is comparatively lower across all the regions, which may point to underexplored research areas in the current research trend. Overall, Figure 3 underscores that each region’s construction industry profile is shaped by local demand drivers and regulatory or market environments, resulting in distinct regional specialisations in industry focus.

5.2. Theoretical Foundations and Industry Applications

Figure 4 serves as an example of the theoretical foundations and industry applications of service quality models through a sunburst chart. Based on Figure 4, several critical trends and limitations emerge. First, the prominence of the SERVQUAL and GAP models across multiple regions—such as the US, Europe, and Asia—reflects their widespread adoption. However, this chart simultaneously exposes methodological over-reliance; the empirical evidence indicates that SERVQUAL’s five-dimensional expectation–perception instrument can understate culturally specific performance attributes in small- and medium-sized projects [10] and mischaracterise process quality determinants in large contractor operations [92]. By aligning these theoretical nodes with the outer ring subindustries, this figure further reveals practical blind spots—particularly within refurbishment projects and facility maintenance—where pronounced service gaps persist, as highlighted by consultant–client mismatch studies in Lagos [6]. The focus on these models, spanning from advanced markets like Australia and Sweden to emerging ones such as Nigeria and Thailand, underscores their global relevance, but also raises questions about their adaptability to the unique contexts and complexities of different construction environments. Consequently, visual mapping not only underscores the necessity of context-tailored refinements and digital feedback loops, but also directs practitioners towards subindustries where targeted service quality enhancements may yield disproportionate reputational gains.

Parasuraman, Zeithaml and Berry [4] developed the SERVQUAL model, which is foundational in many industries, including “Contractor Services,” “Real Estate Broking,” and “Facility Maintenance.” Although SERVQUAL is robust and adaptable across industries, its widespread use may limit the exploration of alternative models that could better address evolving industry challenges. SERVQUAL’s focus on reliability, responsiveness, and tangibles may not capture the nuanced aspects of service quality that are becoming increasingly important in the construction industry, such as sustainability, digital integration, and customisation. Taking together, Figure 2 and Figure 4 reveal that most studies continue to frame service quality through SERVQUAL’s core parameters—reliability, responsiveness, assurance, tangibles, and empathy—with only minor, context-specific additions; this widespread reliance encourages researchers to retrofit complex, digitally driven, and sustainability-focused construction services into a fixed five-dimensional template, rather than devising new constructs that capture the emerging industry realities. Only a limited number of studies have attempted to move beyond SERVQUAL by developing integrated frameworks that embed sustainability and digital innovation into service quality assessment in construction. For instance, a sustainable service quality model tailored for housing integrates environmental criteria alongside functional dimensions, thereby extending beyond SERVQUAL’s original scope [93]. To tackle SERVQUAL’s narrow customer focus, an integrated team satisfaction framework has been developed, which captures the satisfaction of clients and multiple project participants, emphasising collaboration and stakeholder engagement throughout the project lifecycle [94]. Another new approach is the cooperative governance framework for sustainable digital transformation in construction, aligning technological innovation with environmental and social goals [95]. The dominance of the SERVQUAL and GAP models in construction service quality research could hinder innovation by limiting the focus to a generic framework; in fact, the standard five-dimensional SERVQUAL often necessitates modification in this context, such as changing to it have four dimensions in UK professional services [47] or even the expansion of the original GAP model to address construction-specific factors [16]. Moreover, SERVQUAL’s inherent limitations—including a provider-centric, one-way view that neglects clients’ co-creative roles and a lack of accommodation for digitally mediated service delivery—underscore its misalignment with the evolving nature of construction services. Consequently, this research advocates for alternative frameworks better suited to modern built environment contexts. Specifically, five key future research directions have been proposed to address SERVQUAL’s limitations in construction: Service Recovery, Technology and Innovation, Cultural Adaptation, Stakeholder Inclusion, and Sustainability. Service Recovery stresses the systematic management of service failures and corrective actions, an aspect largely overlooked by SERVQUAL, because effective recovery sustains client trust in complex projects [96]. Technology and Innovation broadens the model’s pre-digital scope by integrating tools such as BIM and automation, which can enhance project efficiency and service outcomes [97]. Cultural Adaptation rejects a uniform approach, recognising that expectations and management practices differ across organisational and national settings and must be tailored to secure broad stakeholder satisfaction [98]. Stakeholder Inclusion extends SERVQUAL’s client focus by involving the full project constituency in defining and assessing quality, thereby improving outcomes through communication, engagement, and commitment [99]. Finally, Sustainability embeds environmental and social criteria throughout the project lifecycle, addressing SERVQUAL’s neglect of long-term performance and aligning quality evaluation with the current calls for holistic metrics [100].

6. Discussion

The traditional service quality models (e.g., SERVQUAL and GAP models) provide a baseline for assessing customer expectations and perceptions, focusing on functional quality dimensions, such as reliability, responsiveness, communication, assurance, and empathy. In construction, these models have only been adopted after significant adaptation. Landy, Sousa and Romero [16] suggests that while these functional dimensions are still used as ‘guidelines’, additional technical quality dimensions (e.g., quality of the end-product, design innovation, and aesthetic value) must be introduced to suit the built environment. This reflects the unique nature of construction services; the end-product’s performance (a building or infrastructure) is as critical as the service delivery process. In other words, a project’s success is judged not only by how services are delivered (functional quality), but also by what is delivered (technical quality). The traditional models that largely ignore the technical outcome or treat quality as a static after-the-fact measure struggle to capture this duality. Other researchers observe that a purely compliance-driven view of quality (meeting specs and contracts) is no longer sufficient. The modern approaches emphasize that project quality is determined both by the results and by the process used to achieve them, requiring a broader framework than the old SERVQUAL paradigm [69].

This study also finds two emerging dimensions—digitalisation and sustainability—are rarely addressed in current service quality studies. This is striking given the contemporary importance of these themes; digital transformation is widely acknowledged to be reshaping how services are delivered and what customers expect [101]. Construction clients and end-users are increasingly driving the demand for digitalisation and sustainability as part of construction service quality frameworks. They now expect innovations like virtual reality (VR) design walkthroughs and green building features (e.g., certifications and indoor air quality), and many are willing to pay a premium for such improvements [102]. While integrating digital tools (e.g., BIM and AI) and sustainable solutions can raise the upfront costs, research shows they help avoid costly design changes, rework, and inefficiencies, yielding a better long-term performance and economic benefits [103]. Reflecting these trends, even medium-scale residential developments are increasingly embracing technologies like VR and drones. For instance, the residential industry has been an early adopter of VR in construction [104], and compact inspection drones have proven effective on smaller project sites [105]. However, this study found relatively few publications integrating digital service quality considerations or examining how sustainable practices factor into service quality. For instance, while some researchers have mentioned the potential of digital technology such as Big Data and AI, they rarely include these factors in their models [21,22]. Although not widely covered in the existing studies, technologies such as AI-driven predictive analytics, virtual reality for immersive stakeholder engagement, and integrated risk management tools are likely to redefine service quality expectations in future construction practices. These emerging digital technologies are redefining service quality expectations across the construction industry. Artificial intelligence is already enhancing reliability by automating defect detection and minimising rework, while AI-driven chatbots deliver immediate, personalised stakeholder updates, greatly improving communication efficiency and satisfaction [106].

Coupling Building Information Modelling with immersive virtual reality has yielded a 37% reduction in design conflicts and a 62% rise in stakeholder engagement [107]. Multi-user VR platforms further optimise real-time collaboration and user satisfaction [108]. On-site augmented-reality applications overlay digital data onto physical works, accelerating error detection and knowledge sharing [109]. An AR–BIM defect-inspection framework now identifies cracks in real time, raising quality standards and bolstering client confidence [110]. Fully immersive reality environments let clients explore design alternatives interactively, reducing the number of late changes and increasing satisfaction [111]. Digital safety tools such as VR simulators demonstrably lower accident rates [112], thereby reinforcing schedule reliability and stakeholder trust. Finally, integrated platforms like BIM consolidate scheduling, risk, quality and communication data, cutting errors and waste, while clarifying information flows [113]. BIM-based risk systems enable real-time collaboration and proactive issue resolution [110].

By serving as a central, shared data environment, BIM ensures all stakeholders have access to up-to-date information, which improves reliability (fewer errors due to miscommunication) and responsiveness (the faster identification and resolution of issues). Moreover, the digital integration of formerly siloed processes is proving valuable in risk management. BIM-oriented risk platforms enable real-time data-driven decision making and strengthen stakeholder cooperation in identifying and mitigating risks, leading to more predictable outcomes and smoother service recovery when problems arise (building defect inspection and data management using computer vision, augmented reality, and BIM technology). In summary, these integrated management tools create a more transparent and controlled project environment, thereby boosting clients’ and stakeholders’ confidence in the project team’s reliability, responsiveness, and commitment to quality.

While research on these emerging technologies in construction service quality is still developing, the evidence to date indicates significant positive impacts. As the industry gradually overcomes the adoption challenges, we can expect these technologies to become integral to high-quality construction project delivery, aligning technical execution with superior service outcomes for all project stakeholders. This also shows that our field is ready for new approaches. In summary, generic service models have proven too narrow for construction, prompting calls for more holistic and flexible models tailored to the industry’s complexity. As a result, a project could score high on an SERVQUAL-style evaluation, yet still perform poorly in terms of energy efficiency or social value, a disconnect that is increasingly problematic. Wawak, Ljevo and Vukomanović [69] highlights this issue, for example adding “green” or “sustainable service” dimensions to quality frameworks and identifying sustainability-related critical success factors. Overall, there is recognition that service quality in construction must expand beyond the the traditional client–provider dyad to include the environment and society as key stakeholders. Without incorporating sustainability metrics (e.g., lifecycle carbon, occupant health, and social impact) into quality models, those models remain inadequate for modern construction goals. This neglected dimension underscores the need to update quality assessment tools so that delivering a high-quality service also means delivering a sustainable outcome.

The construction industry faces emerging gaps in service quality related to technology and technical performance. While other industries have rapidly integrated digital tools to enhance service delivery, construction has been slower to adopt digital quality management. Some studies show only the sporadic use of digital technologies in ensuring construction quality, for example, some projects now use laser scanning for faster quality inspections and BIM-based platforms to improve collaboration, but these remain isolated cases [32]. Tang, et al. [114] quantified significant time and cost benefits when using laser scanners for site quality checks, and Ma, et al. [115] demonstrate that integrating Building Information Modelling (BIM) with real-time tracking can improve responsiveness and communication among project stakeholders. However, such innovations are not yet widespread. Overall, digital quality assurance (QA) in the construction industry is still at a low level of technical maturity, with limited actual implementation and evaluation across the industry [32].

Service quality in construction is further complicated by the diverse and sometimes conflicting expectations of different stakeholders. Unlike many service industries where “the customer” is a single entity, construction projects involve multiple parties—clients (owners), contractors, design consultants, end-users, regulators, and more—each with their own definition of quality. This pluralism often leads to tension. In practice, this means there is a trade-offs—contractors may sacrifice some technical finesse to meet deadlines, frustrating clients who expect top-notch workmanship, or a designer’s pursuit of innovative quality may conflict with a contractor’s cost constraints. Recent research illustrates how these tensions play out. For example, Er [116] compare residential clients’ and contractors’ perceptions of “quality” construction. The study found that while the two groups agreed on some basic aspects (such as the importance of good building design, materials, and structural stability), they disagreed on other aspects. Research confirms that such misalignment is a root cause of conflict; disagreements frequently arise from “different views on project objectives (e.g., quality, time, cost, safety)” among project participants [117].

Given the above gaps, it is evident that the traditional models, like the SERVQUAL and the GAP models, are no longer adequate for the construction industry. This Discussion points to the need for a next-generation service quality framework that is holistic, integrative, and context-specific. First, such a framework should seamlessly combine functional quality (service delivery process) with technical quality (final product performance), rather than treating them in isolation. This means quality evaluations would assess not only say a contractor’s responsiveness and communication, but also the engineering performance and innovation of the delivered facility. Second, the framework must embed modern digital integration. As industry practices evolve, quality management is increasingly enhanced by digital tools from BIM coordination meetings to AI-based defect detection. A future framework would include digital readiness and adoption as indicators of service quality (for example, the ability of a project team to use collaborative platforms to prevent errors and respond rapidly to issues). Third, the framework should explicitly incorporate sustainability indicators as a dimension of quality. Sustainable construction is now a key project outcome, and quality models should reflect this by including criteria for environmental performance and social responsibility. Fourth, multi-stakeholder coordination needs to be built into the quality model. The current models predominantly reflect client or contractor viewpoints, overlooking regulators, subcontractors, and end-users [6,44]. Rather than assuming a single “client” viewpoint, the framework should be multi-criteria, capturing the perspectives of various stakeholders, or at least ensuring that their expectations are considered in defining quality outcomes. Finally, future models must address how service failures are handled through mechanisms such as compensation, transparent communication, and rapid remediation. Despite the evidence that the effective handling of service failures can significantly enhance client retention and trust, the literature provides limited insights into systematic service recovery strategies [56,118]. Service recovery is a key driver of client trust and long-term satisfaction, yet is often overlooked in current models [56,118,119]. In the absence of robust recovery-oriented models, construction firms may struggle to implement interventions that mitigate dissatisfaction and restore confidence, thereby curtailing long-term relational value and undermining the overall project outcomes.

7. Proposed Conceptual Framework for Future Research Directions

In response to the identified challenges and the gap summarised in Section 2.3, we explicitly add five new service quality dimensions—(1) Service Recovery, (2) Technology and Innovation, (3) Cultural Adaptation, (4) Stakeholder Inclusion, and (5) Sustainability—to form a holistic framework (see Figure 5). This study proposes a conceptual framework to guide future research directions. Future research on service quality in construction should advance in five interconnected dimensions based on the structure illustrated in Figure 5. The framework further posits that the strategic deployment of digital technologies—principally Building Information Modelling (BIM), digital twins, and the Internet of Things—should facilitate the continuous feedback of service-phase data (for example client commentary, energy consumption, and maintenance records) into the as-built information model, thereby simultaneously enhancing key sustainability indicators (such as embodied and operational carbon) and end-user satisfaction, and ultimately closing the loop between service delivery and product performance.

The proposed conceptual framework linking the five future research areas (yellow ovals in the middle) indicate the future directions introduced by this study to an enhanced service quality model in construction (red oval on the right). Each area addresses the specific limitations of the traditional models (purple boxes on the left). The solid black arrows illustrate the inter-relationships among the new dimensions, while the red dashed arrows indicate how each dimension fills a gap from the legacy frameworks (SERVQUAL/GAP). This network-based diagram highlights that improving service quality in modern construction is a multi-dimensional, interconnected effort, rather than linear gap analysis.

The conceptual model is intended to be flexible and scalable. Construction projects range from small residential builds to large infrastructure programs, and quality expectations can vary across these scales. The framework’s dimensions are broad enough to apply at different project levels—for example, a local residential project might emphasize community participation and cultural nuances of the neighbourhood, whereas a multinational infrastructure project might put more weight on technology integration and formal recovery processes. The model allows for practitioners to map the relevance or intensity of each dimension to their specific context. The proposed conceptual diagram provides a roadmap for future research on service quality in construction. By visualizing how Service Recovery, Tech/Innovation, Cultural Adaptation, Stakeholder Inclusion, and Sustainability interrelate, it guides future research to explore both individual dimensions and their synergies. From a practical standpoint, embracing these research directions could help modernize quality assurance in construction. Companies that apply these strategies, such as implementing digital customer feedback systems for recovery or engaging community representatives in quality planning, may achieve a competitive advantage in high-quality service. Moreover, aligning service quality with sustainability and social responsibility responds to the evolving definition of excellence in the AEC (Architecture, Engineering, Construction) industry, where project success is no longer measured purely by cost and schedule, but also by stakeholder satisfaction and societal impact [120].

Empirical case studies across diverse contexts affirm the relevance of each proposed dimension to construction service quality. For instance, Essien, et al. [121] found that robust service recovery practices, such as systematic post-failure analysis and transparent communication with clients, had a significant positive effect on customer retention.

This demonstrates that effective recovery from service failures directly bolsters client loyalty and quality perceptions. Similarly, Lucien and Amolo [122] report that comprehensive stakeholder inclusion improved project outcomes; active stakeholder involvement, collaboration, and empowerment were correlated with smoother execution and a significantly enhanced project performance.

This finding validates the Stakeholder Inclusion dimension by showing that engaging clients, end-users, and community partners in decision making elevates their level of satisfaction and aligns the project deliverables with the expectations. Moreover, a multinational project in Ghana highlighted the importance of cultural adaptation. Eyiah, Bondinuba, Adu-Gyamfi and Liphadzi [98] revealed that unmanaged cultural diversity within the project team led to misunderstandings, delays in decision making, and communication breakdowns, whereas deliberate diversity management (e.g., cultural sensitivity training and inclusive policies) improved team cohesion and productivity. This underscores that tailoring management approaches to local cultures and enhancing cross-cultural competence are vital for service quality in global projects, as predicted by the Cultural Adaptation dimension. Such real-world evidence shows each dimension of the framework manifesting as a critical success factor in practice, from recovering trust after service failures to engaging stakeholders and adapting to cultural contexts, thereby aligning closely with the framework’s assertions.

Technological innovation and sustainability strategies further emerge as the key quality drivers in recent case studies. In an Indonesian high-rise building project, Technology and Innovation were leveraged through full-spectrum BIM implementation (3D modelling, 4D scheduling, and 6D sustainability analysis). This case study demonstrated that BIM adoption significantly improved service quality outcomes; it optimized the work sequences (boosting efficiency and reducing errors), detected and resolved design clashes early (minimising delays), and supported sustainability by assessing and curbing the project’s carbon footprint [123].

This dual benefit case validates both the Technology and Innovation and Sustainability dimensions, showing how digital tools can achieve a better performance, while meeting environmental goals. Broader studies likewise indicate that these dimensions work synergistically—for example, trust-based relational governance in public–private partnership projects has been empirically linked to greater sustainability performance, especially when coupled with active public stakeholder involvement and managerial innovation [124].

Collectively, such empirical insights lend credence to the five-dimensional framework by illustrating that real-world improvements in construction service quality are achieved when firms invest in service recovery, embrace innovation, adapt culturally, include stakeholders, and pursue sustainable practices in tandem. These proposed directions reflect emerging research themes, but require further validation through stakeholder-informed studies to determine their practical salience in diverse construction settings. These case-derived lessons suggest that future service quality models for construction must be holistic and multi-dimensional. Integrating all the five dimensions can modernise quality assurance—from faster client issue resolution to smarter tech-enabled processes—thereby improving client satisfaction, project efficiency, and societal outcomes in line with the evolving definition of excellence in the AEC industry [98].

8. Conclusions

This systematic review has illuminated the changing environment of construction service quality, showing that the traditional approaches struggle to embrace digital transformation, sustainability, and stakeholder interests. The foundational frameworks (e.g., SERVQUAL and GAP) and later revisions helped the understanding of key quality characteristics like dependability, responsiveness, and tangibles, but today’s market demands are broader and require a more flexible perspective. The network and heatmap investigations showed that the conventional constructions dominate contractor services, real estate broking, and facility maintenance, but they also underscored the necessity of digital preparedness, service recovery strategies, and environmental and social indicators in service quality evaluation. This study’s conceptual framework links five critical research directions: improving service recovery, integrating technology and innovation, customising models for regional contexts, increasing stakeholder participation, and incorporating sustainability and social responsibility. Future service quality models will better reflect the complex interaction of technological efficiency, customer satisfaction, environmental responsibility, and stakeholder engagement by integrating these factors. This paradigm can help practitioners and policymakers improve project results and meet expanding regulatory and social expectations by enabling comprehensive assessment procedures.

Despite these findings, several limitations remain. First, this review used Scopus and the Web of Science, which may have excluded regional or non-English literature. Second, most of our research focused on unique cultural or project settings, limiting its generalisability. Third, the framework is conceptual and requires empirical validation to confirm its effectiveness in real construction scenarios. Future research could undertake pilot studies, action research, or case-based trials across multiple regions to evaluate how integrating advanced digital tools (e.g., BIM and IoT) and sustainability measures influences project performance and predictive capabilities. Such efforts would not only refine the model, but also ensure that construction service quality practices become more inclusive, adaptive, and sustainable in the face of rapid industry changes.