1. Introduction
As construction projects grow increasingly complex and expand in scale, a more diverse set of skills becomes essential for effectively managing project execution and delivery [1]. However, according to perspectives from construction firms on development and management, low efficiency and performance present significant challenges [2] that can impact the management approaches of researchers and workers involved in real projects [3]. The emergence of Industry 4.0 and advancements in technology are poised to introduce new criteria for evaluating project management [4]. This shift entails a departure from traditional management systems that prioritize the performance of projects over the conventional cost–schedule–quality “Iron Triangle” management paradigm [5]. Emphasizing the importance of fostering collaborative partnerships among project stakeholders has become a critical consideration [6]. Moreover, project governance has been utilized to address management system issues in recent years [7].
Numerous studies indicate that project management is often viewed as primarily focused on execution rather than as a comprehensive management system [1,8]. Given the increasing intensity of competition within the construction industry, it is imperative for firms to adopt effective management systems that encompass collaborative behaviors and information exchange among various stakeholders [9,10]. Extensive research consistently demonstrates that effective project governance plays a pivotal role in enhancing project performance [11,12]. This is because project governance not only addresses policy-making issues but also resolves inter-organizational challenges among project partners [13,14]. For example, Fernandes et al. [15] suggested that trust and interpersonal relationships are essential elements in the emergence of project governance in a study of project governance in organizations in Latin America and Europe. They also suggest that the professionalism, centralization, coordination and long-term control of partners have a positive effect on project governance, which contributes to the achievement of successful project management. Consequently, project governance has emerged as a prominent topic in management research. This growing interest is driven by mounting evidence of its substantial positive impact on project performance [16,17].
In China, research has shown that industries other than construction have higher profit rates [18]. Project performance is a critical factor influencing the profitability of construction projects. It is typically associated with meeting predetermined time, budget, and quality standards while fulfilling stakeholder expectations [19]. However, numerous factors beyond project performance can lead to cost overruns and delays in construction projects [20]. These include external factors such as adverse weather conditions, regulatory or market changes, supply chain disruptions, and even political instability, as well as internal factors like inadequate planning, insufficient resource allocation, communication failures, or technical challenges [21].
To address the challenges facing the industry, this study focuses on project performance, aiming to identify key performance indicators and management strategies to mitigate project management risks and prevent cost overruns and delays [20]. Moreover, well-defined project performance indicators can optimize resource allocation, ensure stakeholder satisfaction, enhance project partnerships, and improve social reputation [22]. These factors can strengthen a project’s competitiveness and promote long-term sustainable development. In addition, the International Program in the Management of Engineering and Construction (IMEC) has reported that mega-construction projects with a value of one billion dollars often experience cost overruns of approximately 18% compared to the original cost plan [23]. Cost overruns and delays remain significant indicators of project performance [24,25]. Despite advancements in control tools and cost management technology, the incidence of cost overruns has not decreased over the past 70 years [26,27].
Currently, the majority of academic literature has concentrated on examining the positive influence on project performance, and has highlighted its potential role as a justice mechanism [28,29,30,31]. The contract governance and relationship governance approaches represent two primary research directions within the project governance mechanism framework [32]. The two forms of governance are not mutually exclusive; rather, they complement each other. While contractual governance provides the necessary structure and clarity for operationalizing agreements, relational governance enhances cooperation and facilitates the sharing of information necessary for adapting to changing circumstances [27,33]. It is worth noting that these studies have primarily been conducted within the manufacturing industry and other related sectors [34].
The construction industry, in comparison to other sectors, is characterized by its complexity and involvement of numerous stakeholders [35]. Projects within this industry are temporary in nature, concluding upon the achievement of specific objectives without a stable organizational structure [36]. Effective management methods are essential to address the unique challenges presented by project management in this context. For example, issues such as opportunism and communication efficiency can pose difficulties in project management [37]. Existing research has identified two key gaps in the literature. Firstly, prior studies have primarily focused on governance structures based on internal management policies within companies, overlooking the significance of contractual governance frameworks in enhancing performance outcomes. Mature contractual agreements can help mitigate opportunistic behaviors [38]. Secondly, while some research has explored the relationship between project governance and performance from a relational perspective, findings suggest that performance is influenced by managerial behavior [39]. However, these studies have not adequately addressed issues related to information transfer efficiency and the active role of managers in this process.
While contractual governance and trust can help establish cooperative mechanisms and relationship rules among stakeholders, communication and coordination platforms are still necessary to enhance information exchange and execution efficiency [39]. Borkowski [40] suggested a Building Information Model (BIM), referring to a digital information model used for design, construction, and operational decision-making in building projects. BIM fosters a collaborative environment by enabling real-time interactions among project stakeholders, leading to improved decision-making and problem-solving [38]. BIM enhances visualization through the construction of 3D models, allowing project managers and stakeholders to better understand design and construction aspects, thereby enabling more informed decision-making [41]. This approach improves collaboration, strengthens visualization, and addresses technical issues. The data management capabilities of BIM promote equity among stakeholders by allowing each member to access project data and make necessary corrections [42]. This ensures that changes are visible to all and can be collectively discussed before implementation. Furthermore, BIM provides real-time updates and facilitates centralized data management, creating a collaborative environment that enables more effective communication and coordination among project team members [43]. For example, during the foundation construction phase of engineering projects, the application of BIM technology can optimize construction strategies under both static and dynamic loading conditions, thereby significantly enhancing the efficiency of foundation construction [39].
Therefore, this collaborative approach helps mitigate risks, ensure alignment with strategic objectives, and increase the overall success rate of construction projects [44]. Despite the numerous benefits of BIM technology, its adoption is not without challenges. Significant barriers remain, including lack of skills and experience, access-related obstacles, and the need for continuous training and upskilling of professionals in the Architecture, Engineering, and Construction (AEC) sector [45].
This study addresses two significant gaps in the current research by examining the relationship between project governance and project performance. Specifically, we explore the following research questions:
How does project governance impact project performance in China?
Does the application of BIM technology in project governance have a positive impact on project performance?
Currently, quantitative research examining the relationship between project governance and project performance remains limited, despite the growing importance of this field, and most studies tend to explore it qualitatively [46]. To solve the situation, the study will use empirical analysis methods for the in-depth exploration of the mechanisms of action between project governance and project performance in the Chinese construction industry. Specifically, this paper will analyze how contract governance and relational governance affect project performance. It will also examine the relationship between project governance and project performance, and whether this will be influenced by the level of BIM application.
This study has the following components. In Section 2, we will elaborate on the theoretical background of concepts such as project performance, contract management, and relationship management, as well as relevant knowledge of transaction cost theory (TCT). In Section 3, we will state our research hypotheses. In Section 4, we will discuss the methodology of our study, including data sample selection, data collection process, and evaluation indicators. In Section 5, we will provide an in-depth analysis of the experimental results. In Section 6, we will debate our research findings. Finally, in Section 7, we will summarize the core discoveries of this article and share our conclusions.
2. Literature and Theory Framework
In this section, the research literature and theoretical models related to this topic will be introduced. First, the theoretical framework based on the Cost of Transaction Theory will be analyzed. Secondly, the previous research results will be summarized from dimensions such as project performance, contract governance, and relational governance, and a conceptual model for this study will be established.
2.1. Transaction Cost Economic Theory
The concept of transaction costs was initially proposed by Coase in 1937 and was later further researched and developed into a comprehensive theoretical framework by Williamson [47]. Bounded rationality and opportunism are the assumptions that must be taken into account when applying TCT. The theory posits that in economic activities, individual behavior is not entirely rational [48]. Due to personal differences, there are limitations in information processing and cognitive abilities. These limitations create barriers for participants when acquiring and processing information, thus affecting their ability to make entirely rational decisions [49]. To compensate for these limitations, participants must invest resources in collecting and processing information to ensure the rationality of their decisions [50].
However, opportunistic behavior refers to the possibility that individuals may deliberately disclose incomplete or misleading information, seeking their own interests through means such as deception, misdirection, or camouflage [25]. This behavior adds complexity to economic organizations. Due to the presence of bounded rationality and opportunism, organizational management becomes more complex, and the uncertainty and risk of transactions also increase [51,52]. To address these challenges, participants need to invest more resources to verify the authenticity of information, assess the risk of being deceived, and increase safeguards to reduce transaction uncertainty and the risks it bears [53].
The TCT has a broad research foundation in the field of project governance. Due to the complexity and dynamism of engineering projects, they are often regarded as temporary organizations [54]. In such organizations, participants are usually temporarily assembled, and there may be new members joining or existing members leaving during the course of the project. Because each participant’s interests differ, this dynamism may exacerbate behaviors of bounded rationality and opportunism, which can increase project costs and affect the successful delivery of the project [55].
The use of TCT in the construction sector covers several key areas, such as contract design, risk management, relations with suppliers, and project governance [56]. Firstly, the application of this theory helps create strong contracts that efficiently reduce risks and lower transaction costs [57]. Then, it allows companies to select appropriate governance structures, crucial for additional risk reduction. Moreover, it encourages the development of long-term partnerships with suppliers, which in turn lessens search costs and maintains higher quality standards, ultimately decreasing overall transaction costs [58]. Additionally, the theory supports a fair allocation of contractual responsibilities among all involved parties. This equilibrium, guided by governance frameworks, strengthens cooperation and cuts down the costs related to project execution [59,60]. For a detailed view, please refer to Figure 1.
However, Western scholars have predominantly approached trust from an economic perspective, conceptualizing it as an expectation that the other party will not act opportunistically in the face of uncertainty or risk [61]. Trust is thus a latent psychological state resulting from behaviors or choices that subsequently influences future actions and decisions [62]. In construction projects, trust between owners and contractors can be defined as a mutual willingness to believe in and rely on each other, while also accepting potential economic and other losses during the collaboration process [63]. Among the various elements of relational governance, trust is considered a crucial factor influencing contractor behavior and is often viewed as the core element of relational governance [39]. As a state variable, trust has consistently been regarded as the most fundamental relational norm and holds a significant position in the research framework of project governance [11]. Based on this, this study adopts trust as one of the independent variables in its research model.
In addition, the TCT is frequently used to explore project governance mechanisms [64,65]. Wacker et al. [66] pointed out that TCT is a key theoretical framework that can be used to investigate how contract governance can simultaneously enhance financial returns, boost competitiveness, and reduce performance uncertainty. Based on this theory, Young et al. [67] employed quantitative analysis methods to examine the impact of project governance on the successful delivery of IT projects, finding that explicit contract terms, implicit contract terms, reputation, and trust all played a role in mitigating project risks. For instance, Trygges et al. [68] demonstrated that project governance is a crucial factor influencing project success in supply chains. Their research highlighted the significant role of informal relationships in reducing supply chain costs. Zhou et al. [69] employed TCT to delve into the governance mechanisms of food supply chains. Their findings indicated that both contractual and relational governance mechanisms exerted a positive influence on the digitalization of supply chains. The study found that these two governance mechanisms play a positive role in promoting the food industry to achieve large-scale sustainable development.
The aforementioned research findings consistently demonstrate that project governance is a critical factor in enhancing project success and improving overall project performance. Therefore, this study, based on the TCT, proposes that contract governance (including the completeness and flexibility of contracts) and trust in relational governance will play significant roles in constraining bounded rationality and opportunism. The theoretical model of this study is constructed accordingly, based on the above theory. Based on the above theories, the theoretical model of this study is set up in Section 3.
2.2. Project Performance
Project performance is one of the core issues in the field of engineering project research, and its definition varies depending on the research perspective (mainly divided into outcome-oriented and behavior-oriented approaches) [70,71]. From an outcome-oriented perspective, project performance focuses on work results, which include the achievement of organizational goals, stakeholder satisfaction, and return on investment [72]. Therefore, project performance can be defined as the measure of the results produced under specific times and work contents. However, some scholars argue that project performance is not equivalent to outcomes alone, as outcomes are influenced by many external factors [55,73]. They believe that project performance should also include the methods or means of achieving organizational goals. Additionally, some scholars consider project performance to be part of organizational performance, reflecting the overall operation of the enterprise or organization and requiring the establishment of a comprehensive model to evaluate whether the organization and individuals have achieved the planned goals [74].
Existing research indicates that the assessment of performance indicators commonly uses project maturity models, excellence performance evaluation models, and key performance evaluation models [75]. In the Chinese context, the key performance evaluation model is the most widely used method [76]. The key performance evaluation model, based on key success factors, can start from the overall strategic goals of the project, subdividing the project into different levels and stages, thus developing more precise evaluation indicators [71]. The multi-faceted nature of engineering projects necessitates a nuanced approach to performance evaluation. The proposed framework acknowledges the complexity of these projects by recognizing the distinct requirements and objectives associated with each phase of development [72]. This stage-specific and hierarchical goal-setting approach allows for a more accurate and comprehensive assessment of project performance, accounting for the diverse stakeholders involved and their respective targets. Such a tailored evaluation method can potentially lead to more effective project management and improved overall outcomes in complex engineering endeavors [63].
The evaluation model of project performance has shifted from the traditional “iron triangle” of cost, quality, and time to include more success criteria, such as customer satisfaction, stakeholder satisfaction, and knowledge management [77]. This study uses a comprehensive indicator of economy, efficiency, and effectiveness to evaluate project performance, considering not only the impact of individual factors on the project but also the overall impact of the project on society, the environment, and investment efficiency. This evaluation system is more suitable for the current transformation of Chinese engineering projects from extensive to refined development goals.
2.3. Contractual Governance
Contract governance is a formal mechanism in project governance that involves the establishment of a system of legally binding contracts, including clear instructions, regulations, and rules, to define the powers and duties of the parties involved [39]. In engineering project management, formal contract mechanisms are a common practice and serve as an important means for stakeholders in temporary organizations to quickly establish trust. Therefore, for such temporary organizations, contracts play a significant role in constraining the behavior of members, especially in the handover of construction processes [33,78]. Heydari et al. [42] have demonstrated that the application of BIM and block-chain technologies to construction material procurement can facilitate the achievement of contractual governance objectives and enhance decision-making flexibility through effective information management. The TCT suggests that stakeholders can use contract terms to protect their interests and limit the opportunistic behavior of others [79]. At the same time, it can resolve disputes and conflicts, thus improving transaction efficiency.
Therefore, this study will focus on two core principles of contract governance: contract completeness and contract flexibility [80]. Contract completeness refers to whether the contract terms are comprehensive and clearly define the rights and obligations of the parties involved [80]. Contract flexibility, on the other hand, considers the contract’s adaptability in the face of changes and instability in the project organization, for example, project adjustments, delays, and price changes; that is, looking at whether the contract can flexibly respond to these changes [50].
Based on previous context, this study endeavors to investigate how contract governance can more effectively restrain the actions of project participants, diminish transaction costs, and enhance the comprehensive performance of the project.
2.4. Relational Governance
Relational governance is an informal mechanism in project governance that complements formal contract governance [69]. It primarily refers to the binding force of social Relational rules and social norms in transaction activities, guiding and adjusting the behavior of both parties through the application of a series of relational norms to coordinate their relationships [81]. There is a complementary relationship between contractual governance and relational governance. Contracts can provide a foundation for building trust and cooperative relationships [82], while relational norms can fill the gaps in incomplete contracts. Strong relationships can facilitate contract negotiations. However, an excessive emphasis on contracts may limit the flexibility of management activities, while over-reliance on relationships can lead to unclear responsibilities [83]. Unlike contract governance, which relies on laws and contract clauses, relational governance relies on informal mechanisms such as mutual dependence and cooperation to respond to environmental changes, coordinate transactions, and ensure the smooth progress of transactions, using relational behaviors such as trust between participants to curb the occurrence of opportunistic behavior [84].
Moreover, trust is an important indicator in the relationships between participants, as it can prompt both parties to address issues in cooperative relationships based on shared values, enhance adaptability to environmental changes, and reduce transaction and communication costs [85,86]. In projects, trust is key to the development of relationships between service providers, suppliers, contractors, and clients [86]. Once a foundation of trust is established between the parties, cooperation becomes smoother [87]. Trust is also regarded as an alternative to control mechanisms, which can reduce agency costs inherent in project delivery methods [88]. Moreover, trust, as a soft constraint, can promote cultural integration among participants, drive different trading parties to form unified values and goals, establish cooperative relationships of shared risks and benefits, and thus enhance supply chain performance [85]. In research on EPC (Engineering, Procurement, and Construction) projects, scholars have found that trust in relational governance has a significant positive impact on the performance of EPC projects [89]. Based on TCT and Social Exchange Theory, some scholars have found, in China’s situation-relational society, that trust in the informal relationships between cooperative parties can effectively mitigate the impact of environmental and project uncertainties, thereby improving project performance [27,90].
2.5. BIM Application Level
In recent years, BIM technology has been recognized as an innovative tool and data management solution that is used in all phases of the project lifecycle [91]. BIM technology digitizes building information and integrates the goals and plans of different participants in the project [92]. Therefore, BIM platforms can resolve obstacles arising from differing goals and ideas among project participants, which often become barriers to achieving project objectives [93]. In contract governance, costs can be categorized into contract development costs and contract management costs. BIM technology primarily focuses on reducing contract management costs [94]. When contract changes, unforeseen events, or other unpredictable circumstances arise, the BIM platform can efficiently establish communication channels and rapidly develop feasible implementation plans [42]. Consequently, it minimizes resource waste and delays caused by ineffective communication and coordination, indirectly improving project management efficiency [93]. In other words, the lack of trust and information sharing is a key issue leading to inefficient communication, which in turn affects project performance.
Additionally, the data from the planning, design, construction, and operational aspects of the project will be recorded into the BIM platform [93]. Meanwhile, it can also effectively resolve conflicts and promote collaboration among participants [91]. In other words, the BIM technology can reduce costs, enhance communication, and improve information sharing among stakeholders throughout the project lifecycle. (Figure 2). For example, Marinho et al. [95] argue that BIM applications improve collaboration efficiency by minimizing errors, building trust, and eliminating information asymmetry. In addition, the application of BIM cause a reduction in misunderstandings and conflicts that will improve collaboration and communication efficiency. Rajabi et al. [96] indicate that the key evaluation criteria for BIM capabilities, such as the quality of BIM models, the efficiency of BIM collaboration, and the utilization of BIM data, are significant factors influencing project performance. Additionally, Wu et al. [97] point out that BIM maturity refers to the comprehensive level of an organization or project team in adopting and implementing BIM technology. It reflects the maturity of the organization in terms of processes, technology, personnel, management, and strategic planning in BIM application [98,99]. Sadeghi et al.’s [100] research highlights the potential application of BIM combined with advanced 3D optimization techniques in transportation engineering projects. This integration can significantly reduce the negative environmental impacts of construction, such as by mitigating the effects of vibrations on the surrounding environment during the construction process.
BIM technology has demonstrated extensive applicability in healthcare-related engineering projects. Arjanaki et al. [101] investigated the efficacy of BIM in uncertain conditions, revealing its capacity to provide substantial support for real-time control system engineering in the healthcare sector. Their findings indicate that BIM significantly enhances real-time decision-making processes in these complex environments. Complementing this research, El Fathi et al. [102] explored BIM’s utility in managing intermittent projects within the healthcare industry. Their study demonstrated that BIM technology facilitates improved data control in such projects, enabling the more effective formulation of strategies for periodic project review and adjustment. Collectively, these research outcomes underscore the potential of BIM technology in addressing the need for adaptive control mechanisms in project management. By enhancing real-time decision-making and improving data control for intermittent projects, BIM offers promising solutions to the challenges of managing complex, dynamic healthcare engineering projects.
Therefore, compared to traditional management models, project governance has achieved more effective management by establishing structured decision-making mechanisms and control systems [36]. BIM technology has significantly impacted project management practices and outcomes by improving collaboration, enhancing communication, and providing better project visualization [45]. Therefore, when examining the mechanisms through which project governance influences project performance, it is essential to consider the supportive role of BIM technology.
While the moderating role of BIM technology in relational governance and knowledge collaboration has been well-established, empirical studies examining how these elements collectively influence project performance from the integrated perspective of contract governance and relational governance remain scarce [38]. Therefore, this study aims to propose and test hypotheses to investigate the mechanisms through which these factors impact project performance [38,98].
3. Hypothesis Development
3.1. Contract Governance and Project Performance
The core of contractual governance lies in balancing contract integrity and flexibility. Integrity emphasizes clarity, comprehensiveness, and the enforceability of terms, while flexibility ensures that the contract can adapt to a constantly changing external environment [103]. Poppo et al. [104] found that more detailed and specific contract terms help to improve the effectiveness of project execution, highlighting the close relationship between contract integrity and project efficiency.
Therefore, flexibility in contracts refers to the adaptability of contractual terms when facing unforeseen events, allowing effective risk control during project execution [105]. When unexpected events occur, project participants can fulfill their responsibilities according to the contract terms rather than focusing on short-term opportunistic gains [106,107]. In other words, contract flexibility creates room for adjustments, allowing for sufficient communication in the face of uncertainties or issues, thereby fostering an effective cooperative environment and enhancing transaction performance. Based on this, the following hypotheses are proposed:
Contract completeness positively influences project performance in construction projects.
Contract flexibility positively influences project performance in construction projects.
3.2. Relational Governance and Project Performance
Relational governance is an informal governance mechanism grounded in trust and commitment, emphasizing interaction and cooperation between organizations during project execution [95]. Extensive research in the field of engineering projects has demonstrated that trust is founded on positive expectations of the other party’s behavior and a willingness to accept associated risks [23]. For instance, trust between the owner and contractor is reflected in their mutual reliance, even in the face of potential economic or other losses during cooperation [108]. Moreover, trust helps establish a sense of identity among project participants, strengthens unity, and promotes information exchange [62], thereby reducing opportunistic tendencies and enhancing the quality and efficiency of cooperation, ultimately improving project performance [66].
Hence, this study proposes the following hypothesis:
Trust positively influences project performance in construction projects.
3.3. The Moderating BIM Application Level
Previous studies have shown that the application of BIM technology plays a positive role in the relationship between project governance and performance [109,110,111]. For instance, in BIM-supported EPC projects, contract completeness effectively facilitates collaboration, thus positively influencing project outcomes [112]. However, in real-world cases such as the Shanghai Expo project, the failure to clearly define the responsibilities of different parties in the contract led to inefficient information transmission, negatively impacting cooperation. This underscores the importance of contract flexibility in a BIM environment [113]. Moreover, studies indicate that in the construction phase of PPP projects, BIM technology helps to maintain a positive correlation between contract flexibility and project performance [114]. Trust significantly enhances team collaboration efficiency, and the level of BIM application serves as a key moderating factor that supports broader social goals, such as promoting transportation equity [115,116].
Therefore, they found that higher levels of BIM application positively affect team cooperation efficiency and contribute to improved project performance. Collectively, these studies suggest that the moderating role of BIM application levels in the relationships between contract completeness, flexibility, trust, and project performance is not only theoretically plausible but also supported by empirical evidence and practical applications.
Therefore, this study proposes the following hypotheses:
The effect of contract completeness on project performance with the BIM application level is higher in construction projects.
The effect of contract flexibility on project performance with the BIM application level is higher in construction projects.
The effect of trust on project performance with the BIM application level is higher in construction projects.
All hypotheses from this study are shown in Figure 3.
4. Research Method
The specific research methods for this study are shown in Figure 4.
4.1. Sampling and Data Collection Procedures
This study adopts a positivist quantitative research method and uses a cross-sectional survey design to collect data. Online survey questionnaires are widely used in project management research because they can reduce costs, save time, and ensure that data collection is based on statistical principles. Given China’s extensive infrastructure development, rapid urbanization, diverse regional characteristics, and widespread adoption of emerging technologies, a unique and dynamic context for studying project management practices is provided. This study will focus on middle- and senior-level managers in China’s construction industry who have been involved in large-scale projects with investments exceeding RMB 1 billion [9]. Participants will include representatives from owners, contractors, design firms, and cost consulting companies.
To ensure that the target population has a rich project experience, individuals were selected from different construction companies in Sichuan Province, including from economically developed cities such as Chengdu, Mianyang, and Yibin. These companies engage in engineering projects such as subways, housing, water conservancy, and bridges. To ensure the validity and reliability of the survey instrument, three experienced project managers with a minimum of ten years in the construction industry and a proven track record of managing at least three large-scale projects were invited to conduct a rigorous review. Their extensive industry knowledge was instrumental in assessing the survey’s alignment with industry practices. To maintain data quality and mitigate bias, the study was confined to professionals within a specific industry sector. Questionnaire items underwent multiple rounds of refinement by experts and scholars with engineering backgrounds, with careful attention paid to consistency between Chinese and English versions. Data cleaning procedures were implemented to identify and remove inconsistencies, errors, and outliers. Common method bias analysis and other statistical techniques were employed to assess data quality and address potential biases.
The data collection process took 2 months and was divided into three stages: first, identifying suitable research subjects; second, sending them emails; and finally, sending the survey questionnaire to those who responded to the email to ensure the quality and response rate of the questionnaire. Based on G-POWER software (version 3.1) testing, the sample size was determined to be 138, which helps to ensure the effectiveness of the sample size. The variables of the survey questionnaire in this study were derived from previous research literature; therefore, they have good reliability and validity.
4.2. Measurement
This study measures 25 indicators, all derived from the existing literature. This ensures that the measurements meet academic requirements. Since the measurements selected for this study come from the English-language literature, the indicators were carefully translated and reviewed to maintain their accuracy and relevance. The first step was to translate them into Chinese, and then to invite experts to review them to make them more understandable to the research subjects and to prevent any ambiguity in their responses. Finally, the Chinese translations were compared with the original English to make appropriate adjustments to the original items and to conduct a pre-test to check the feasibility of the items. Each item in the questionnaire used a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).
The variables and their measurement indicators in this study are contract completeness ([78]), contract flexibility [31], trust [27,62], BIM application level [38], and project performance [117,118]. Table 1 lists the measurement items.
4.3. Data Analysis
In this study, data analysis was primarily undertaken employing SPSS 26 software and PLS-SEM (Partial Least Squares Structural Equation Modeling) techniques. SPSS 26 was used for demographic descriptions and common bias analysis of the sample data, while PLS-SEM assessed the strength of relationships between research variables. Notably, PLS-SEM has been widely used in management research within construction projects over the past few decades, exploring causal relationships among variables and demonstrating its practical validity.
This study chose PLS-SEM over AMOS (Analysis of Moment Structures) for two main reasons. Firstly, the relatively small sample size might not meet the multivariate normal distribution requirements. Secondly, the conceptual model and non-normal variables are better suited to PLS-SEM, which is more adaptable to exploratory models. In contrast, AMOS is more appropriate for confirmatory theoretical research, which demands a more rigorous theoretical foundation. Therefore, Smart-PLS 4.0 software was used for structural modeling and evaluation.
Data analysis in this study was conducted in two phases. In the first phase, SPSS software was used to analyze the data. Initially, the statistical functions were employed to conduct a demographic analysis of the sample data. Subsequently, factor analysis and reliability analysis were performed to assess the validity and reliability of the data. Finally, the IBM SPSS AMOS functionality was utilized to conduct a common method bias analysis. In the second phase, Smart-PLS software was employed to assess the measurement model and structural model, thereby validating the research hypotheses.
5. Results and Findings
5.1. Descriptive Model
A total of 210 samples were collected, and after eliminating incomplete samples, 175 valid samples remained. After the data collection phase was completed, this study conducted a demographic analysis using SPSS software with the objective of verifying whether the sample’s demographic distribution was scientifically and reasonably structured. The specific results of the analysis are presented in Table 2.
5.2. Common Method Bias
To reduce common method bias in data collected during the data collection process, we employed several strategies. Firstly, the questionnaire content was well designed. Secondly, each question was clearly articulated to prevent confusion. Finally, volunteers’ information will be kept confidential. Additionally, the items in the questionnaire were disordered, preventing volunteers from predicting the objectives of the study. Furthermore, statistical methods were used to test for common bias, such as Harman’s single-factor test, used to examine common method bias issues. The results indicate that without rotation, five factors had eigenvalues greater than 1, with the first factor explaining 21.344% of the variance, which is less than 50%. Therefore, common method bias will not impact subsequent hypothesis testing.
Additionally, this study employed a Confirmatory Factor Analysis (CFA) model to further investigate the issue of common method bias. The results indicated that the single-factor model had the poorest fit (χ2/df = 3.732, TLI = 0.438, CFI = 0.485, IFI = 0.494, RMSEA = 0.195), further confirming that common method bias is not a significant concern in this study. At the same time, given the limitations of the Harman single-factor test, such as its lack of sensitivity, we drew on previous research by comparing the model with the method factor included in the original model [115]. Although the five-factor model with the method factor (χ2/df = 1.825, TLI = 0.921, CFI = 0.957, IFI = 0.937, RMSEA = 0.055) outperformed the four-factor model (original hypothesized model), the CFI of the five-factor model only increased by 0.001 compared to the hypothesized model, which is below the 0.050 threshold. In summary, this study does not exhibit significant common method bias.
5.3. Measurement Model
This study conducted tests on the measurement model using indicators of structural reliability and validity. We utilized SPSS Statistics 26.0 software to perform Cronbach’s α coefficients, KMO values, and Bartlett’s test of sphericity. The results indicated that all variables surpassed the threshold of 0.7 for Cronbach’s α and KMO (Kaiser–Meyer–Olkin) values, and Bartlett’s test of sphericity was significant (Table 3), affirming good reliability of the scale. Subsequently, we employed Smart PLS software to further assess the reliability of each variable, analyzing factor loadings, Cronbach’s α coefficients, CR (Composite Reliability), and AVE (Average Variance Extracted), with specific data detailed in Table 4 and Table 5. Finally, the AVE values were used to test the discriminant validity. As shown in Table 6, the square root of AVE for each diagonal variable is greater than the standardized correlation coefficients of the other variables, indicating good discriminant validity among the variables.
In this paper, Smart PLS 4.0 was used to fit the main effects of governance mechanism on project performance, and the fit degree of the model was tested according to the GOF (goodness-of-fit) index calculation formula () (1) proposed by Tenenhaus et al. [119]. After calculation, GOF = 0.579, which is greater than the standard value (0.36) of the complex model.
5.4. Structural Model
This research employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct a comprehensive structural model examining hypothesized relationships between contract completeness, contract flexibility, trust, BIM application, and project performance. To assess the explanatory power of the structural model, we employed the coefficient of determination R2 (R-squared) for endogenous constructs and used the cross-validated redundancy measure Q2 (cross-validated redundancy measure) to evaluate forecasting capability. According to the results depicted in Figure 5, the R2 value was 0.604, exceeding the threshold of 0.50 [52]. Concurrently, the Q2 value was 0.376, greater than 0, indicating that the model achieved an acceptable level of predictive performance. The specific results are shown in Figure 5.
The main path indicators and the index values of the adjustment effect in this study are shown in Table 7.
The results from the table suggest that if the p-value for H4 is greater than 0.05, this suggests that the moderating effect of BAL adoption between project performance and contract completeness is not valid. However, both H5 (β = 0.257, P = 0.012 *) and H6 (β = 0.266, P = 0.015 *) show positive path coefficients, with p-values less than 0.05, indicating that the level of BIM technology application moderates the relationship between contract flexibility and trust.
In order to further analyze the effect of moderating, three moderating effect diagrams were drawn using simple slope analysis method. The results are detailed in Figure 6, Figure 7 and Figure 8.
6. Discussion
From the perspective of TCT, this study aims to investigate the relationship between project governance mechanisms and project performance. Furthermore, it examines the moderating effect of BIM adoption levels on the relationship between project governance and project performance. The following discussion presents the findings of this research.
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CC and CF in governance can enhance project performance.
H1 and H2 suggest that contract governance effectively enhances project performance. Firstly, during project implementation, the establishment of contracts enables stakeholders to devise comprehensive terms around project objectives. These terms extensively constrain behaviors, effectively curbing opportunistic actions and thereby facilitating successful project delivery. Secondly, the construction process is inherently dynamic, necessitating contract flexibility beyond rights and obligations to accommodate various engineering changes and swiftly coordinate work processes. Such flexibility helps mitigate dispute costs arising from unforeseen events, thus contributing to cost savings. Lastly, contracts, as formal legal relationships, hold legal validity and enable the effective supervision of participants’ conduct. Project stakeholders mutually monitor each other based on contractual provisions to ensure the protection of respective interests, thereby promoting smooth project delivery.
-
2.. TR mechanisms in relational governance can enhance project performance.
H3 indicates that informal relationships play a crucial role in project performance. Despite the ability of formal contractual relationships to constrain participant behavior during project development, their limitations are apparent. In contrast, trust-based informal governance significantly enhances cooperation between organizations and reduces communication and coordination costs, thereby improving project management efficiency. This conclusion aligns with findings by Lee et al. [112].
Furthermore, trust contributes to participants’ sense of security within the organization. When projects encounter challenges, trust enables participants to promptly take effective measures and willingly assume risks that benefit the organization’s overall interests or improvements, rather than focusing solely on personal gain. These behaviors contribute to enhanced project performance.
Trust dynamics among project partners can be categorized into three phases: (a) an initial trust phase based on reputation, past experience, or contractual agreements; (b) an in-process trust phase shaped by interactions among participants during the project; and (c) a final trust phase determined by project outcomes [28,37]. Initial trust significantly influences project success. High initial trust fosters an open and collaborative environment, laying a solid foundation for project execution. Conversely, low initial trust may lead to excessive reliance on formal contracts, reducing project flexibility [98]. The sustainability of project outcomes influenced by initial trust is contingent upon factors such as project complexity, duration, external environment, organizational culture, and trust mechanisms [99,111].
Moreover, trust is influenced by cultural and organizational differences. First, in highly competitive industries, establishing trust is particularly challenging. Organizations may prioritize short-term gains over long-term relationships, creating an atmosphere of distrust. This competitive environment can hinder open communication and collaboration, both of which are crucial for effective relational governance. Second, diverse organizational cultures may affect how trust is perceived and developed. For instance, in cultures that emphasize hierarchy, the open communication necessary for building trust may be difficult to achieve, whereas more egalitarian cultures may find it easier to establish relational norms. These cultural differences can lead to misunderstandings and conflicts, ultimately impacting project performance. The effectiveness of trust mechanisms may also vary depending on contextual factors such as project complexity and organizational maturity. Therefore, relying solely on trust may be insufficient; formal contracts may also be necessary to mitigate the risks associated with opportunism and ensure accountability.
Therefore, overall, the application of trust mechanisms in relational governance significantly enhances project performance by fostering cooperation, reducing costs, and strengthening participants’ commitment to the organization. This effectively promotes successful project implementation and management.
-
3.. BAL adoption plays a moderating role between project governance and project performance.
H4 suggests that the data platform established by BIM technology enhances information transparency, which effectively reduces ambiguities and fosters trust among all stakeholders. Furthermore, the real-time availability of data enables project managers to make informed decisions more quickly, thereby strengthening the trust that various stakeholders place in them.
H5 and H6 indicate that the information-sharing mechanisms based on the BIM platform positively influences the establishment of trust among participants and facilitates contract flexibility. However, in terms of contract integrity, BIM’s moderating effect is less pronounced because dynamic information exchange among participants during contract formulation is relatively limited. Trust and contract flexibility are critical factors during project management, influenced by various constraints.
Therefore, effective information exchange and coordination mechanisms are crucial for enhancing inter-organizational workflows, helping to avoid information barriers and making the project management process more efficient and transparent. This suggests that leveraging BIM technology effectively can optimize information flow and improve collaboration efficiency, thereby enhancing the viability of governance mechanisms and improving overall project performance.
7. Conclusions and Implications
This study adopts an inter-organizational cooperation perspective to investigate the moderating effect of BIM technology on the relationship between project governance and project performance. By integrating BIM technology into the relationship model, this research not only expands the theoretical framework in the field of project management but also provides new insights for engineering practice. The findings contribute to a more comprehensive understanding of how technological innovations can influence project outcomes within complex organizational contexts.
Empirical results revealed several significant findings in this study. Firstly, the completeness of contractual governance positively influences project performance; higher levels of contractual completeness enable parties to significantly improve project outcomes through reduced disputes, enhanced risk management, clearer communication, and more efficient project execution. Secondly, contractual flexibility aids project managers and participants in better adapting to unforeseen circumstances or changes in project requirements, fostering a more collaborative environment between owners and contractors, reducing the likelihood of costly disputes, allowing for the effective management of project uncertainties, and providing ample space for decision-making by project managers. Consequently, construction projects are likely to achieve superior performance outcomes. Thirdly, trust, established as a mechanism between participants under common goal requirements, ensures that parties believe their interests will not be compromised and will act proactively for project success out of genuine commitment, significantly enhancing management efficiency and performance. Notably, both contractual flexibility and trust are moderated by the influence of BIM technology on project performance, suggesting that BIM implementation may alter the dynamics of how flexibility and trust impact project outcomes. However, unlike contractual flexibility and trust, contractual completeness is less susceptible to the influence of construction activities, primarily because it involves the formulation and execution of initial terms, which are typically established before the active construction phase.
These findings contribute to the development of a comprehensive framework for project governance, exploring the intricate interrelationships among contractual governance, relational governance, BIM technology, and project performance in project management. The results empirically demonstrate the moderating role of BIM technology in the proposed model. This study not only supports and extends existing research but also provides a systematic explanation of the mechanisms through which BIM technology influences project performance, thereby advancing our understanding of project management dynamics in the context of technological innovation.
7.1. Theoretical Implications
This article delves into the mechanisms by which project governance influences project performance, and conducts empirical research while examining the moderating role of BAL. Firstly, the study adopts a dynamic perspective, using BIM technology as a moderating variable to explore how information dissemination and effective communication influence project performance through contract governance and trust. Previous studies have primarily focused on the positive influence of contract governance and relational governance on project performance. Traditional project governance often adopts a static perspective, viewing projects as linear processes from initiation to closure [120]. This paper proposes a dynamic governance perspective, emphasizing the flexibility and adaptability of governance processes [121]. Conventionally, governance focuses on foundational setup in the initiation phase, detailed planning in the planning phase, process control in the execution phase, and evaluation and knowledge sharing in the closure phase. In contrast, dynamic governance advocates for the continuous adjustment of governance strategies throughout the project lifecycle to respond to evolving internal and external environments. The advantages of dynamic governance lie in its ability to more promptly identify and mitigate project risks, better meet the evolving needs of stakeholders, and ultimately enhance project success.
In addition, drawing insights from the application of BIM technology in project governance, policymakers should focus on the following measures at the contractual governance level: establishing cloud-based BIM platforms to reduce hardware investment for enterprises and improve resource utilization; unifying BIM data standards to promote data interoperability between different software platforms, enhancing data exchange quality and efficiency; establishing a robust data security management system, including implementing strict data security protocols and hierarchical access control to ensure the security and confidentiality of BIM data; and strengthening BIM technology training and improving talent cultivation mechanisms to meet the industry’s demand for high-quality BIM professionals. At the relational governance level, emphasis should be placed on establishing effective communication platforms during the construction process to encourage the active participation of all stakeholders in the BIM platform and enhancing the awareness of all participants regarding the significance of BIM platforms in daily work to promote their proactive application.
Thus, this study fills a gap in explaining certain phenomena in engineering practice. Secondly, from an integrative standpoint, the research underscores the importance of BIM technology in facilitating efficient information sharing among project participants to enhance organizational communication and foster trust, thereby ensuring successful project delivery. Lastly, the study employs the lens of TCT to microscopically examine novel approaches to curbing opportunism and reducing costs, offering new theoretical perspectives and practical guidance for project governance mechanisms.
7.2. Managerial Implications
The research suggests several important management perspectives for project managers. Firstly, the impact of contract governance mechanisms on project performance is crucial. Project managers should ensure thorough communication and establish well-defined contract terms to safeguard the rights of all parties and mitigate the negative effects of opportunistic behavior on project performance. Moreover, contract terms should be designed with flexibility to address complexities and external environmental changes during project implementation, thus reducing additional costs and improving execution efficiency.
Secondly, establishing a trust mechanism among project management personnel is paramount. Trust not only fosters collaboration among organizational members but also reduces collaboration risks and accelerates the implementation of project measures and decisions. Project managers should address the work needs and challenges of team members, provide support and advice, and actively build and maintain trust through positive actions to enhance project management efficiency and performance. To address disparities in stakeholder engagement, the following strategies are recommended: (1) Establish a mechanism for incorporating stakeholder input at the decision-making level to enhance the scientific basis of decisions. (2) Strengthen information sharing and collaboration, encouraging active stakeholder participation in project decision-making to optimize BIM information management processes. (3) Develop a robust coordination mechanism to effectively manage conflicts, balance stakeholder interests, and improve overall project performance [12]. (4) Implement a balanced approach that combines relational governance with contractual governance can effectively enhance trust. Relational governance fosters collaboration, while contractual governance provides the necessary framework for resolving disputes and ensuring accountability. (5) Establish feedback mechanisms, which can help organizations assess the effectiveness of trust-building initiatives, and make necessary adjustments. Continuous evaluation ensures that trust mechanisms remain relevant and effective across different cultural contexts.
Lastly, enhancing information exchange mechanisms among project organizations is crucial, and particularly the leveraging of BIM technology platforms to improve the efficiency and quality of information sharing. The project manager should develop a BIM-based information sharing strategy to motivate the project participants to actively share information, taking into account the specific culture of the stakeholders [44]. For example, firstly, the hierarchical structure of rights is rigorous, emphasizing the authority and seniority of the individual. Secondly, there is a collectivist orientation that emphasizes the idea of harmony and the expectation of balancing the interests of different participants, and finally, there is the impact of personal relationships and influence on the implementation of decisions. This approach enhances cooperation and trust among internal organizational members, thereby further improving overall project performance.
In summary, these management insights not only help project managers better understand and address challenges in project governance but also guide them in adopting effective strategies and measures to enhance the capability for successful project delivery and performance.
8. Limitations and Future Research
This study primarily investigates the impact of project governance on performance, focusing on the efficiency of information exchange and collaboration among different participants within an organization. Cooperation processes across different industries and regions are inevitably influenced by individual and organizational behaviors. By establishing contractual rules and relational norms, it is possible to effectively control and regulate behaviors during the cooperation process, thereby fostering collaboration and improving project performance.
However, the study has some limitations. Firstly, the data collection was limited to Sichuan Province, where BIM technology adoption is relatively low, thus limiting the generalizability of the findings. For instance, the low adoption rate might lead to an overestimation of BIM’s effects, as early adopters often experience more pronounced benefits. The unique economic and technological landscape of Sichuan Province may not be representative of other regions, potentially skewing the perceived impact of BIM on project performance. Secondly, the study did not adequately account for external factors influencing contract governance and trust mechanisms, such as policy factors and environmental uncertainties. This omission could affect the interpretation of project performance in several ways: Policy factors, like local government initiatives or national regulations, might be the actual drivers behind some of the observed performance improvements, rather than BIM implementation alone. Environmental uncertainties, such as market fluctuations or supply chain disruptions, could mask or amplify the perceived effects of BIM on project outcomes. Finally, the challenges faced by BIM technology have not been considered, such as high initial investment costs, data interoperability, data security, and the number of personnel, as well as the integration of future technological advances into the project governance framework.
Future research directions should address these shortcomings. Firstly, due to varying levels of development across different regions in China. it is recommended that future studies broaden their sample size to enhance the applicability of research findings in various contexts. For example, the Pearl River Delta region demonstrates a high level of utilization of BIM technology. Therefore, it will provide more robust comparative data for the study. Such an approach would not only improve the generalizability of the research conclusions but also better capture the variations in technological implementation across different regional settings. Secondly, future studies should incorporate policy formulation and project context influences, including policy factors, market transparency, internal and external environmental factors, and collaboration quality as mediators or moderators. Finally, the influence of BIM technology on project performance from alternative theoretical perspectives should be explored, particularly in information integration and sharing.
Furthermore, the role of project governance should be validated in other industries, such as transportation infrastructure projects and the construction industry supply chain, as well as in different countries or regions. Given that relational norms are particularly emphasized in the context of Chinese characteristics, it is necessary to examine how organizations and individuals in other regions perceive relational behavior.
In light of the rapid development of information technology, future research can explore the integration of BIM with emerging technologies such as blockchain, artificial intelligence (AI), and machine learning in the context of project governance. For example, the integration of blockchain technology can facilitate the creation of smart contracts, enabling the automation and verification of project contracts. Meanwhile, the incorporation of AI and machine learning can enhance the automation of project processes and improve decision-making. The integration of these information technologies holds the potential to revolutionize project governance. These information technologies will significantly accelerate the sustainable development of large engineering projects.
Conceptualization, B.Y. and N.L.S.N.; methodology, B.Y.; software, B.Y.; validation, B.Y.; formal analysis, B.Y.; investigation, B.Y.; resources, N.L.S.N.; data curation, B.Y.; writing—original draft preparation, B.Y.; writing—review and editing, B.Y. and N.L.S.N.; visualization, N.L.S.N.; supervision, N.L.S.N.; project administration, N.L.S.N. All authors have read and agreed to the published version of the manuscript.
The data that support the findings of this study are available from the corresponding author, N. Nie, upon reasonable request.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 6. The moderating effect of BIM application level on the relationship between contract completeness and project performance.
Enlarge this image.Figure 7. The moderating effect of BIM application level on the relationship between contract flexibility and project performance.
Enlarge this image.Figure 8. The moderating effect of BIM application level on the relationship between trust and project performance.
Operating variables and survey questionnaire.
| Variables | Operationalization | Survey Questionnaire | References |
|---|---|---|---|
| Project Performance (PP) | Eight items | 1. After the project is completed, the cost target is achieved. | [ |
| Contract Completeness (CC) | Four items | 1. The contract includes detailed specific clauses (project features, rewards, breach resolution methods, etc.). | [ |
| Contract flexibility (CF) | Five items | 1. The contract clauses set a floating range to deal with potential risks or uncertainties. | [ |
| Trust (TR) | Five items | 1. We believe that our project partners have sufficient capability to execute their tasks. | [ |
| BIM Application level (BAL) | Three items | 1. Managed 3-D environments are established in a separate discipline BIM model in which data exchange is mainly on the basis of proprietary of exchange formats. | [ |
Demographics and profiles of respondents.
| Item | Frequency | % | Item | Frequency | % |
|---|---|---|---|---|---|
| Gender | Level of Education | ||||
| Male | 102 | 58 | Below Undergraduate | 4 | 2 |
| Female | 73 | 42 | Bachelor’s Degree | 85 | 49 |
| Total | 175 | 100 | Master’s Degree | 52 | 30 |
| Doctor’s Degree | 34 | 19 | |||
| Total | 175 | 100 | |||
| Age | Working Time | ||||
| 30–39 | 111 | 63 | Less than 5 years | 93 | 53 |
| 40–49 | 40 | 23 | 5–10 | 34 | 19 |
| 50–59 | 18 | 10 | 10–15 | 32 | 18 |
| Over 60 | 6 | 3 | More than 15 years | 16 | 9 |
| Sector | Project Type | ||||
| Leader | 30 | 17 | Building Construction Projects | 95 | 54 |
| Project Manager | 88 | 50 | Infrastructural Construction Projects | 54 | 34 |
| Others | 57 | 33 | Others | 26 | 15 |
Reliability results of variables.
| Item | Rotated Factor Loading | KMO |
|---|---|---|
| BAL1 | 0.859 | 0.760 |
| BAL2 | 0.864 | |
| BAL3 | 0.774 | |
| CC1 | 0.859 | 0.809 |
| CC2 | 0.864 | |
| CC3 | 0.774 | |
| CC4 | 0.859 | |
| CF1 | 0.755 | 0.845 |
| CF2 | 0.794 | |
| CF3 | 0.681 | |
| CF4 | 0.782 | |
| CF5 | 0.780 | |
| PP1 | 0.784 | 0.875 |
| PP2 | 0.738 | |
| PP3 | 0.660 | |
| PP4 | 0.813 | |
| PP5 | 0.729 | |
| PP6 | 0.911 | |
| PP7 | 0.655 | |
| PP8 | 0.757 | |
| TR1 | 0.791 | 0.847 |
| TR2 | 0.743 | |
| TR3 | 0.800 | |
| TR4 | 0.838 | |
| TR5 | 0.824 |
Cross-loading.
| Item | BAL | CC | CF | PP | TR |
|---|---|---|---|---|---|
| BAL1 | 0.938 | 0.323 | 0.291 | 0.549 | 0.486 |
| BAL2 | 0.929 | 0.209 | 0.296 | 0.456 | 0.478 |
| BAL3 | 0.929 | 0.195 | 0.194 | 0.576 | 0.488 |
| CC1 | 0.262 | 0.903 | 0.524 | 0.510 | 0.235 |
| CC2 | 0.068 | 0.702 | 0.277 | 0.271 | 0.084 |
| CC3 | 0.271 | 0.900 | 0.386 | 0.626 | 0.339 |
| CC4 | 0.218 | 0.867 | 0.446 | 0.469 | 0.253 |
| CF1 | 0.263 | 0.440 | 0.826 | 0.353 | 0.207 |
| CF2 | 0.208 | 0.347 | 0.809 | 0.301 | 0.287 |
| CF3 | 0.235 | 0.499 | 0.847 | 0.438 | 0.418 |
| CF4 | 0.140 | 0.325 | 0.733 | 0.243 | 0.152 |
| CF5 | 0.237 | 0.275 | 0.770 | 0.271 | 0.201 |
| PP1 | 0.438 | 0.550 | 0.379 | 0.854 | 0.426 |
| PP2 | 0.583 | 0.442 | 0.363 | 0.827 | 0.517 |
| PP3 | 0.542 | 0.417 | 0.435 | 0.787 | 0.462 |
| PP4 | 0.456 | 0.513 | 0.286 | 0.882 | 0.512 |
| PP5 | 0.375 | 0.641 | 0.374 | 0.825 | 0.360 |
| PP6 | 0.429 | 0.340 | 0.273 | 0.854 | 0.347 |
| PP7 | 0.437 | 0.474 | 0.374 | 0.761 | 0.290 |
| PP8 | 0.520 | 0.469 | 0.283 | 0.859 | 0.492 |
| TR1 | 0.554 | 0.285 | 0.339 | 0.576 | 0.924 |
| TR2 | 0.345 | 0.409 | 0.265 | 0.483 | 0.826 |
| TR3 | 0.408 | 0.158 | 0.284 | 0.397 | 0.842 |
| TR4 | 0.337 | 0.091 | 0.263 | 0.301 | 0.806 |
| TR5 | 0.543 | 0.217 | 0.261 | 0.349 | 0.853 |
Confirmatory factor analysis.
| Latent Variable | Cronbach’s Alpha | CR | AVE |
|---|---|---|---|
| BAL | 0.925 | 0.935 | 0.868 |
| CC | 0.870 | 0.925 | 0.717 |
| CF | 0.860 | 0.890 | 0.637 |
| PP | 0.936 | 0.938 | 0.692 |
| TR | 0.906 | 0.942 | 0.725 |
Discriminate validity (Fornell–Larcker criterion).
| Constructs | BAL | CC | CF | PP | TR |
|---|---|---|---|---|---|
| BAL | 0.932 | ||||
| CC | 0.262 | 0.847 | |||
| CF | 0.276 | 0.489 | 0.798 | ||
| PP | 0.571 | 0.583 | 0.418 | 0.832 | |
| TR | 0.520 | 0.292 | 0.336 | 0.518 | 0.851 |
Path coefficient results.
| Hypothesis | Path | β | T | P | ƒ2 | Effect Size | Result |
|---|---|---|---|---|---|---|---|
| H1 | CC→PP | 0.387 | 2.290 | 0.022 * | 0.178 | Large | Support |
| H2 | CF→PP | 0.355 | 2.256 | 0.010 * | 0.156 | Large | Support |
| H3 | TR→PP | 0.285 | 2.029 | 0.000 *** | 0.044 | Small | Support |
| H4 | BAL × CC →PP | 0.118 | 0.913 | 0.361 | 0.020 | Small | Reject |
| H5 | BAL × CF →PP | 0.257 | 2.219 | 0.012 * | 0.049 | Small | Support |
| H6 | BAL × TR →PP | 0.266 | 2.363 | 0.015 * | 0.061 | Small | Support |
Notes: *** p < 0.001, * p < 0.05.
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; Nina Lee See Nie 2
1 College of Civil Engineering, Sichuan University of Science & Engineering, Zigong 643000, China;
2 Graduate School of Business, SEGi University, Petaling Jaya 47810, Selangor, Malaysia